Katzilla MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.2
Disambiguation5/5
Each tool is clearly distinct, targeting a specific data source or API within a well-defined domain (e.g., agriculture__usda-nass for crop statistics vs. agriculture__usda-fooddata for nutrient info). The naming convention with double underscores separates domains and sources, preventing overlap and making tool purposes unambiguous. An agent can easily differentiate between tools like 'crime__fbi-most-wanted' and 'crime__recap-docket' based on their descriptions.
Naming Consistency5/5
All 274 tools follow a strict and consistent naming pattern: 'domain__source-description' using snake_case throughout (e.g., 'economic__fred-series', 'environment__noaa-cdo'). This pattern is uniformly applied across all tools, with no deviations in style or structure, making the set highly predictable and readable.
Tool Count2/5
With 274 tools, the count is excessive for a single server's scope, even though the server aims to aggregate many data sources. This volume overwhelms typical agent workflows and exceeds reasonable scoping (25+ tools is considered heavy). While each tool is valuable individually, the collective size makes the server feel like a bundle of many mini-servers rather than a coherent set.
Completeness5/5
The tool set is exceptionally complete for its intended purpose of providing access to diverse public data APIs. It covers a wide range of domains (agriculture, crime, economics, health, etc.) with comprehensive coverage within each, including search, retrieval, and query operations. There are no apparent gaps; each tool serves a specific data source, and the set collectively supports extensive data access workflows without dead ends.
Average 4.3/5 across 274 of 274 tools scored.
See the Tool Scores section below for per-tool breakdowns.
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true). The description adds valuable context beyond this: it specifies the return envelope format ('Katzilla envelope { data, quality, citation }'), explains quality scoring ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format and components. It's front-loaded with core functionality and avoids unnecessary fluff, though it could be slightly more concise by integrating some details (e.g., source info) more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the return structure and data quality aspects, which complements the structured fields effectively without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'type' fully documented (enum: ['hot', 'iced'], default: 'hot', description: 'Coffee type'). The description doesn't add any parameter-specific semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get coffee drink recipes and information.' It specifies the verb ('Get') and resource ('coffee drink recipes and information'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools, which are all data-fetching APIs but from different domains (e.g., agriculture, consumer, crypto).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the source ('Sample APIs (Free API)') and update frequency ('updates monthly'), which helps gauge data freshness. However, it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., other food/drink APIs) or any exclusions, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source ('NREL Building Component Library'), update frequency ('updates monthly'), return format ('Katzilla envelope'), and details about quality scores and citation information. This enhances behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose and followed by source, update frequency, and return details. Every sentence adds value, with no redundant information. It could be slightly more concise by integrating some details, but overall it is efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search functionality with two parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete enough. It explains the purpose, source, update frequency, and return structure, compensating for any gaps. With annotations and output schema handling behavioral and return aspects, the description provides adequate contextual information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('query' and 'limit') well-documented in the schema. The description does not add any additional meaning or syntax details for these parameters beyond what the schema provides, such as examples of query terms or explanation of limit implications. Baseline 3 is appropriate as the schema carries the full burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search', 'Find') and resources ('Building Component Library from NREL', 'building components, materials, and assemblies for energy modeling'). It distinguishes itself from sibling tools by focusing on NREL's building component data, which is unique within the energy category compared to other tools like 'energy__eurostat-energy' or 'energy__nrel-census-rate'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('for energy modeling') and mentions the source and update frequency, but does not explicitly state when to use this tool versus alternatives or provide any exclusions. No sibling tools are directly referenced for comparison, leaving usage guidance at an implied level rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 data hash'), which aids in understanding output behavior and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured into two sentences: one stating the purpose and return metadata, and another detailing the source, update frequency, and output format. It is front-loaded with the core functionality and avoids unnecessary repetition, though the second sentence is slightly dense with multiple details packed together.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (read-only list operation with two parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It explains the purpose, data source, update frequency, and detailed return structure, compensating for any gaps without needing to reiterate schema or annotation information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('limit' and 'query'). The description does not add any parameter-specific semantics beyond what the schema provides (e.g., no examples or formatting details for the query). With high schema coverage, the baseline score of 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List available dataset collections'), resource ('from the Copernicus Climate Data Store (CDS)'), and scope ('Returns collection metadata including temporal and spatial extent'). It distinguishes itself from sibling tools by focusing on Copernicus datasets, unlike other environment tools (e.g., weather, air quality).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the agent label '[Environment & Air Quality Agent]' and mentions the data source and update frequency ('updates daily'), but does not explicitly state when to use this tool versus alternatives (e.g., other Copernicus tools or environmental data sources). No exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. While annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, the description discloses that data 'updates daily' (freshness behavior) and specifies the return format as 'the Katzilla envelope { data, quality, citation }' with details about quality scores and citation content. This provides important operational context about data currency and output structure that annotations don't cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and scope, the second covers data source, update frequency, and return format. Every element serves a purpose, though the parenthetical '[Environment & Air Quality Agent]' adds little value and the detailed citation explanation could be slightly condensed. Overall, it's appropriately sized and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with filtering), rich annotations covering safety and idempotency, 100% schema coverage, and existence of an output schema (implied by the return format description), the description is complete enough. It covers purpose, data scope, source, update frequency, and detailed return format—providing all necessary context for an agent to understand when and how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all 4 parameters (table, zipCode, state, limit) with descriptions, defaults, and enums. The description adds minimal parameter semantics by listing the specific table names in its opening sentence, but this largely repeats what's in the schema's table parameter description. The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search EPA's Envirofacts multi-system database covering TRI facilities, RCRA hazardous waste, drinking water systems, Superfund sites, and air facilities.' It specifies the verb ('Search'), resource ('EPA's Envirofacts multi-system database'), and scope (listing specific database tables). This distinguishes it from sibling environment tools like EPA AQS or NOAA CDO that focus on different environmental data sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by listing the database tables covered (e.g., TRI facilities, RCRA hazardous waste) and mentioning daily updates, suggesting it's for current environmental data queries. However, it doesn't explicitly state when to use this tool versus alternatives like EPA AQS (air quality) or EPA ECHO (compliance data), nor does it provide any exclusion criteria or prerequisites for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description adds value by disclosing behavioral traits beyond annotations: it specifies the source ('Mail.gw'), update frequency ('monthly'), and return format details ('Katzilla envelope' with quality scores and citation info). No contradiction with annotations is present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every sentence provides essential information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, rich annotations, and an output schema, the description is complete. It covers purpose, source, update frequency, and return format, compensating for any gaps and providing sufficient context for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics, explaining the return structure ('Katzilla envelope') and its components, which adds meaningful context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get available temporary email domains from Mail.gw.' It specifies the verb ('Get'), resource ('temporary email domains'), and source ('Mail.gw'). However, it does not explicitly differentiate from sibling tools, as none appear to be related to email domains or Mail.gw, though this is implied by the unique context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'Free API' and 'updates monthly,' suggesting it's for retrieving static, periodically updated data. It does not provide explicit when-to-use guidance, alternatives, or exclusions, leaving the agent to infer based on the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the return format (Katzilla envelope with data, quality, citation), explains what quality scores represent (freshness/uptime/confidence), and details citation contents (source URL, license, SHA-256 hash). While annotations cover read-only/non-destructive/idempotent aspects, the description provides important implementation details about data structure and audit capabilities.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose and data characteristics, the second explains return format and components. While slightly dense, every element serves a purpose without redundancy. It could be slightly more streamlined but remains appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers data source, update frequency, return format structure, and quality metrics - addressing what the structured fields don't explicitly state while avoiding redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (year with range/lag context, limit with default). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation without enhancing parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving top military spending countries ranked by defense expenditure from the SIPRI database. It specifies the data source (World Bank/SIPRI), update frequency (annual), and distinguishes itself from sibling tools by focusing on companies/expenditure ranking rather than other military data types like transfers or expenditure details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the mention of 'Top military spending countries' and annual updates, but doesn't explicitly state when to use this tool versus alternatives like 'military__sipri-expenditure' or 'military__sipri-transfers'. It provides some implicit guidance through the data scope but lacks explicit comparison or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source (FBI Public Domain), update frequency (daily), and details about the return envelope structure (Katzilla with quality scores and citation info), which helps the agent understand data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and key details, and the second explains the return format and metadata. Every sentence adds essential information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (retrieving structured data with metadata), the description is complete. It covers the purpose, source, update frequency, and return format. With annotations covering behavioral traits and an output schema implied by the mention of the Katzilla envelope, no critical information is missing for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters (page and limit) with ranges and defaults. The description does not add any parameter-specific semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Retrieve') and resource ('FBI Most Wanted list'), including details like fugitive details, images, and reward information. It distinguishes itself from sibling tools by focusing on FBI data, unlike other crime tools like courtlistener or recap-docket which handle court records.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving FBI Most Wanted data, but does not explicitly state when to use this tool versus alternatives or any prerequisites. It mentions the source and update frequency, which provides some context, but lacks explicit guidance on scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (Mempool.space), license (AGPL-3.0), update frequency ('real-time'), and detailed output structure ('Katzilla envelope' with quality scores and citation details). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and output; the second explains the output format in detail. Every sentence adds critical information (e.g., source licensing, output envelope structure), with no redundant or vague phrasing, making it highly concise and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no nested objects), rich annotations (covering read-only, non-destructive, idempotent, open-world), and the presence of an output schema, the description is complete. It adds necessary context like source details, real-time updates, and output format explanation, compensating for any gaps and ensuring the agent has sufficient information to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'period' fully documented in the schema (including enum values and default). The description adds minimal semantic value by mentioning 'for a given time period', which is already implied by the schema. No additional parameter details are provided, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get Bitcoin mining pool statistics') and resources ('from Mempool.space'), and distinguishes it from sibling tools by specifying the exact data returned ('pool names, block counts, and hashrate share'). It explicitly mentions the source (Mempool.space) and the output format (Katzilla envelope), making it highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the time period parameter ('for a given time period') and the real-time updates, but it does not explicitly state when to use this tool versus alternatives (e.g., other crypto tools like crypto__blockchain-stats or crypto__mempool-fees). No exclusions or prerequisites are provided, leaving usage guidance at an implied level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide: it discloses the return format ('Katzilla envelope { data, quality, citation }'), explains what quality scores represent ('freshness/uptime/confidence'), describes citation contents ('source URL, license, SHA-256 data hash'), and mentions update frequency ('updates annual'). This complements the annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first establishes purpose and scope, the second details the return format and behavioral aspects. Every element serves a purpose with zero wasted words, making it easy to parse while being information-dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (data querying with quality metrics), rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It explains the return structure, quality scoring, citation details, and update frequency—exactly what's needed beyond the structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters. The description doesn't add any additional parameter semantics beyond what's in the schema descriptions, so it meets the baseline of 3. It doesn't compensate for gaps because there are none to compensate for.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query', 'Retrieve') and resources ('U.S. Census Bureau American Community Survey (ACS) 5-year estimates', 'population, income, housing, and demographic variables by geography'). It distinguishes itself from sibling tools like 'demographics__census-economic-indicators' by focusing specifically on ACS data rather than other census datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the mention of 'Demographics & Population Agent' and specifying ACS 5-year estimates, but doesn't explicitly state when to use this tool versus alternatives like 'demographics__census-economic-indicators' or other demographic tools. No explicit exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the data source (U.S. Census Bureau, Public Domain), update frequency (monthly), and the return structure (Katzilla envelope with data, quality, citation details including freshness/uptime/confidence scores and audit hash). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope with specific examples, and the second details the return format and data quality. Every element (source, update frequency, return structure) serves a clear informational purpose without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, 100% schema coverage, annotations covering key behaviors, and an output schema implied by the return format description), the description is complete. It covers purpose, data source, update frequency, and return structure, compensating adequately where annotations and schema may not fully convey context (e.g., data quality and citation details). With output schema information provided in the description, no gaps remain for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (indicator with enum values and year with range). The description does not add any parameter-specific semantics beyond what the schema provides—it mentions general indicator types but does not elaborate on parameter usage, defaults, or interactions. Given the high schema coverage, a baseline score of 3 is appropriate as the description adds no extra parameter insight.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: retrieving 'Monthly and quarterly economic indicators from the US Census Bureau' with specific examples (retail sales, construction spending, manufacturing, housing starts, and trade). It clearly distinguishes this from sibling tools like demographics__census-acs (which focuses on ACS data) or economic__bls-series (Bureau of Labor Statistics data), establishing a unique scope for Census Bureau economic indicators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying the data source (U.S. Census Bureau) and update frequency (monthly), and mentions the agent context ('Demographics & Population Agent'). However, it does not explicitly state when to use this tool versus alternatives like economic__bea-gdp or economic__fred-series for similar economic data, nor does it provide exclusion criteria or prerequisites for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'), which aids in understanding output behavior and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format explanation. Every sentence adds value: the first states the action and source, the second specifies update frequency, and the third details the output structure and components. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the return format and quality metrics, which complements the structured data, ensuring the agent understands what to expect without needing to detail every output field.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter (tableName, frequency, year) well-documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as examples of table names beyond 'T10101' or clarification on year ranges. Baseline 3 is appropriate since the schema handles parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'GDP and national income data from the Bureau of Economic Analysis (BEA)', specifying the exact data source. It distinguishes from siblings like 'economic__bls-series' or 'economic__fred-series' by focusing on BEA-specific GDP/income data, not other economic indicators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving BEA GDP/income data but does not explicitly state when to use this tool versus alternatives like 'economic__eurostat-gdp' or 'economic__world-bank'. It mentions the source and update frequency ('updates daily'), which provides some context, but lacks direct comparisons or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it specifies the data source authority, update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation components. This provides useful behavioral context without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and scope, the second details return format and source information. Every element adds value without redundancy, and key information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has comprehensive annotations (read-only, non-destructive, idempotent, open-world), 100% schema coverage, and an output schema (implied by the description of return format), the description provides complete context. It covers purpose, data source, update frequency, and return structure, making it fully adequate for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain seriesId patterns or year constraints further). With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Fetch time-series data'), the source ('Bureau of Labor Statistics'), and the scope ('Covers CPI, unemployment, employment, and other labor statistics'). It clearly distinguishes this as a BLS-specific tool among many economic data siblings like FRED or World Bank tools, providing specific verb+resource+scope differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for BLS labor statistics data but doesn't explicitly state when to use this versus alternatives like 'economic__fred-series' or 'economic__bea-gdp'. It mentions the data source and coverage but lacks explicit guidance on tool selection criteria or exclusion scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the data source (UN Comtrade), update frequency (daily), return format (Katzilla envelope with data, quality, citation), and details like quality metrics and citation components (source URL, license, SHA-256 hash). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, with two sentences that efficiently convey the tool's purpose, source, update frequency, and return structure. Every sentence adds critical information without redundancy, making it easy for an agent to parse and understand the tool's functionality quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, 100% schema coverage, annotations, and an output schema), the description is complete enough. It covers the data source, update behavior, and return format in detail. With annotations handling safety and idempotency, and the output schema presumably defining the Katzilla envelope structure, no significant gaps remain for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter well-documented in the schema (e.g., defaults like '842 = USA', '0 = World', 'M = imports'). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining trade flow codes or commodity codes in more detail. However, the baseline is 3 since the schema adequately covers parameter meanings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Query') and resource ('international trade data from the United Nations Comtrade database'). It distinguishes itself from siblings by specifying the exact data source (UN Comtrade) and domain (international trade), unlike other economic tools like economic__bea-gdp or economic__bls-series that focus on different economic indicators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source and update frequency ('updates daily'), but does not explicitly state when to use this tool versus alternatives. It lacks guidance on prerequisites, exclusions, or comparisons to sibling tools like economic__wto-trade or trade__eurostat-trade, leaving the agent to infer usage from the domain.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations: it specifies the data source (World Bank), update frequency (monthly), license (CC BY-4.0), and return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enriches the agent's understanding of data reliability and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose and scope, the second details source, updates, and return format. Every sentence adds critical information without redundancy, making it front-loaded and zero-waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, read-only operation) and rich annotations/output schema, the description is complete. It covers purpose, data scope, source, update frequency, license, and return structure—compensating well for any gaps. With an output schema present, it doesn't need to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter well-documented in the schema (e.g., country as ISO code, indicator examples). The description adds minimal parameter semantics beyond the schema, only implying indicator types (GDP, population) without new syntax or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Fetch development indicators') and resources ('from the World Bank API'), covering key domains like GDP, population, and health. It distinguishes itself from sibling tools (e.g., economic__bea-gdp, economic__fred-series) by specifying it's for World Bank data, not other economic sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by listing indicator types (GDP, population, etc.) and mentioning monthly updates, but it lacks explicit guidance on when to use this tool versus alternatives like economic__bea-gdp or economic__fred-series. No when-not-to-use or prerequisite information is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the update frequency ('updates monthly'), describes the return structure ('Katzilla envelope { data, quality, citation }'), and explains quality and citation details, which aids in understanding data reliability and auditability without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by coverage, source, update frequency, and return format in a single, efficient sentence. Each part adds value without redundancy, and the structure is logical, making it easy to parse for an AI agent. No sentences are wasted on repeating schema or annotation information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return structure, which, combined with structured fields, provides sufficient context for an agent to use the tool effectively. The output schema handles return values, so the description need not explain them further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter well-documented in the schema (e.g., indicator codes, economy codes, year format). The description does not add parameter-specific semantics beyond what the schema provides, such as explaining indicator code meanings or usage examples. However, it implies parameter relevance through the coverage domains, maintaining the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Fetch') and resource ('international trade statistics from the World Trade Organization (WTO) API'), and distinguishes it from siblings by specifying coverage domains ('merchandise trade, services, and tariffs') and source ('World Trade Organization (Public)'). This provides precise differentiation from other economic tools like economic__comtrade or economic__world-bank.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the coverage domains and source, but does not explicitly state when to use this tool versus alternatives like economic__comtrade or international__wits-trade. It mentions the return format and quality metrics, which helps understand the tool's output, but lacks explicit guidance on selection criteria or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the source ('College Scorecard – U.S. Department of Education'), update frequency ('updates annual'), and detailed return structure ('Katzilla envelope { data, quality, citation }') with quality metrics and audit features. This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the tool's purpose and source, and the second details the return format and its components. Every sentence adds critical information (e.g., data source, update frequency, return envelope structure) with zero wasted words, making it highly concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return structure, compensating for areas where structured data might be sparse (e.g., behavioral context like data freshness and audit trails). With annotations and output schema handling safety and return values, the description fills remaining gaps effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('state' and 'limit') fully documented in the input schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining interactions between parameters or typical use cases. The baseline score of 3 is appropriate since the schema carries the full parameter documentation burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query school demographic data from the College Scorecard API including race/ethnicity breakdowns by state.' It specifies the verb ('query'), resource ('school demographic data'), source ('College Scorecard API'), and scope ('by state'), and distinguishes it from its sibling 'education__college-scorecard' by focusing specifically on demographic breakdowns rather than general scorecard data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source and update frequency ('updates annual'), but does not explicitly state when to use this tool versus alternatives like its sibling 'education__college-scorecard' or other demographic tools. It provides some operational context but lacks explicit guidance on selection criteria or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source ('National Park Service (Public Domain), updates annual'), describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality metrics ('freshness/uptime/confidence') and citation details ('source URL, license, SHA-256 data hash'). No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a structured manner. Every sentence adds value: the first defines the action and scope, the second specifies the source and update frequency, and the third details the return format and its components. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search operation with quality metrics), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the data source, update frequency, return structure, and audit features, compensating well for any gaps not covered by structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('query' and 'limit') well-documented in the input schema. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't clarify query syntax or limit implications). Baseline score of 3 is appropriate since the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('search', 'find') and resources ('National Park Service lesson plan database', 'educational resources about national parks, history, science, and the environment'). It distinguishes itself from sibling tools like 'education__college_scorecard' or 'education__uk_education' by focusing on NPS lesson plans rather than other educational datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating it searches for 'educational resources about national parks, history, science, and the environment', but it does not explicitly state when to use this tool versus alternatives (e.g., other education tools like 'education__college_scorecard' or general search tools). No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, and a SHA-256 data hash for audit'). It also notes data update frequency ('updates annual'), which is not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose and scope, the second details the return format and metadata. Every sentence adds value (data source, return structure, quality metrics, citation details) with no redundant or vague language. It is appropriately sized and front-loaded with core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple read-only query with two parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context like data source, update frequency, and return format details, compensating adequately where structured fields might not fully inform the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (state and limit). The description does not add any parameter-specific semantics beyond what the schema provides (e.g., no extra details on state codes or limit behavior). According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving 'water quality assessment data from EPA ATTAINS' with specific details like 'impaired waters, pollutant sources, and cleanup status by state' and that it 'Tracks which waterways meet Clean Water Act standards.' It distinguishes from siblings by specifying the data source (EPA ATTAINS) and type (water quality), unlike other environment tools like EPA AQS (air quality) or NOAA CDO (climate data).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for water quality data by state, but does not explicitly state when to use this tool versus alternatives (e.g., other EPA tools like epa-aqs for air quality). It mentions the data source and update frequency ('updates annual'), which provides some context, but lacks explicit guidance on prerequisites, exclusions, or comparisons to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the data source (NOAA CO-OPS, Public Domain), update frequency (daily), and details about the return structure (Katzilla envelope with quality scores and citation info), which helps the agent understand data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential details about the source, updates, and return format. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension by an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (environmental data retrieval), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context like data source, update frequency, and return structure details, ensuring the agent has enough information to use the tool effectively without needing to rely solely on structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (station ID and product enum). The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. Since the schema is comprehensive, a baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get latest tidal, water level, temperature, and wind data') and resources ('from a NOAA CO-OPS station'), and it distinguishes itself from siblings by specifying the NOAA CO-OPS data source and the Katzilla envelope return format, which is unique among the listed environment tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving environmental data from NOAA stations, but it does not explicitly state when to use this tool versus alternatives like other environment tools (e.g., environment__canada-weather, environment__openaq). No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool's name and data source.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the data source ('Chess.com'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation components. No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, 100% schema coverage), rich annotations, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return format details, providing sufficient context for an agent to use the tool effectively without needing to explain basic parameters or output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'username' parameter fully documented in the schema. The description does not add any additional meaning or syntax details beyond what the schema provides, such as username format constraints or examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a Chess.com player's stats including ratings for rapid, blitz, bullet, and puzzle categories.' It specifies the verb ('Get'), resource ('player's stats'), and scope ('ratings for rapid, blitz, bullet, and puzzle categories'), distinguishing it from sibling tools which cover diverse domains like agriculture, consumer data, or other entertainment APIs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning the source ('Chess.com') and update frequency ('updates daily'), but does not explicitly state when to use this tool versus alternatives. It lacks guidance on prerequisites (e.g., valid username) or comparisons to other tools, leaving usage context inferred rather than clearly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source (data.gouv.fr), license (Licence Ouverte / Open License), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source, license, update info, and return format details. Every sentence adds value: the first states what it does, the second provides metadata, and the third explains the output structure. No wasted words, and it's structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (geocoding with quality metrics), the description is complete: it covers purpose, source, licensing, update frequency, and detailed return format. With annotations covering safety and idempotency, and an output schema presumably detailing the Katzilla envelope structure, no critical gaps remain. It adequately prepares the agent for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('query' as address search and 'limit' as max results). The description does not add any parameter-specific semantics beyond what the schema provides (e.g., no examples of query format or limit constraints). Baseline 3 is appropriate since the schema fully documents parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search for French addresses') and resources ('using the data.gouv.fr address API'), and distinguishes it from siblings by specifying the geographic scope (French addresses) and data source (data.gouv.fr). This is more specific than generic geocoding tools like geo__census-geocoder or geo__nominatim in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the mention of 'French addresses' and the data source, but does not explicitly state when to use this tool versus alternatives (e.g., for non-French addresses or other geocoding needs). It provides no exclusions or direct comparisons to sibling tools, leaving the agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the data source (OpenStreetMap Nominatim with ODbL license), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details), which helps the agent understand data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by source and return details. Every sentence adds value: the first defines the tool, the second specifies source and updates, and the third explains the return structure. There is no wasted text, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (geocoding with external data), rich annotations (covering safety and idempotency), and the presence of an output schema, the description is complete. It adds necessary context like data source, license, update frequency, and return format, which complements the structured fields without redundancy, ensuring the agent has sufficient information for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (query and limit). The description does not add further details about parameter semantics beyond what the schema provides, such as query formatting examples or limit implications. Given high schema coverage, a baseline score of 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for locations using OpenStreetMap Nominatim geocoding service.' It specifies the verb ('search'), resource ('locations'), and service source, distinguishing it from siblings like geo__census-geocoder or geo__geocode-xyz by naming the specific geocoding service (Nominatim).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for location searches but does not explicitly state when to use this tool versus alternatives. It mentions the source (OpenStreetMap Nominatim) and daily updates, which provides some context, but lacks explicit guidance on scenarios or comparisons with sibling tools like geo__osm-overpass for more complex queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations: it specifies the data source ('National Park Service (Public Domain)'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation information. While annotations cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the tool's purpose and source, and the second explains the return format and its components. Every sentence provides essential information without redundancy, making it front-loaded and easy to parse for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, 100% schema coverage, annotations covering key behavioral hints, and an output schema implied by the return format description), the description is complete. It covers purpose, data source, update frequency, and return structure, which suffices for an agent to understand and invoke the tool effectively without needing additional explanations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents the two parameters ('query' for search and 'limit' for max results). The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or limit constraints, so it meets the baseline expectation without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose as 'Search US National Parks using the National Park Service API', which includes a specific verb ('Search'), resource ('US National Parks'), and data source ('National Park Service API'). It clearly distinguishes itself from sibling tools like 'geo__census-geocoder' or 'geo__nominatim' by focusing exclusively on national parks data rather than general geocoding or mapping services.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source ('National Park Service API') and update frequency ('updates daily'), but does not explicitly state when to use this tool versus alternatives. It lacks guidance on prerequisites, limitations, or comparisons with other geography-related tools in the sibling list, leaving the agent to infer appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it discloses the data source ('OpenStreetMap Overpass (ODbL)'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation information. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format and its components. Every sentence adds critical information (e.g., source license, return structure, quality scores) with zero wasted words, making it front-loaded and highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (geospatial querying), rich annotations (covering safety and idempotency), 100% schema coverage, and the presence of an output schema (implied by return format details), the description is complete. It covers purpose, source, update frequency, and return structure, leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters (lat, lon, radius, tag, limit). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining tag syntax or radius constraints. Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Query OpenStreetMap Overpass API to find nearby points of interest by tag.' It specifies the verb ('query'), resource ('OpenStreetMap Overpass API'), and scope ('nearby points of interest by tag'), clearly distinguishing it from sibling tools focused on other domains like agriculture, crime, or economics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for geography/geolocation queries with tags but does not explicitly state when to use this tool versus alternatives (e.g., other geo__ tools like geo__nominatim or geo__census-geocoder). It mentions the source and update frequency, which provides some context, but lacks explicit guidance on exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it specifies the data source (Analytics.usa.gov), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with core functionality, followed by source and return details in two efficient sentences. Every sentence adds value: the first defines purpose and scope, the second specifies source, updates, and output structure, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (data access with parameters), rich annotations (read-only, idempotent, open-world), and output schema (implied by return format description), the description is complete. It covers purpose, source, update frequency, and output structure, compensating for any gaps without needing to repeat schema details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters (report, limit, after). The description does not add parameter-specific semantics beyond what the schema provides, such as explaining report types or date formats. Baseline 3 is appropriate as the schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'access[es] federal website analytics data from the Digital Analytics Program (DAP) including visits, pageviews, and users,' specifying the verb ('access'), resource ('federal website analytics data'), and data types. It distinguishes from siblings by focusing on U.S. government analytics, unlike other tools in the list that cover agriculture, crime, or other domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for accessing federal analytics data, but does not explicitly state when to use this tool versus alternatives (e.g., other government data tools like 'government__datagov' or 'government__site-scanning'). It mentions the source and update frequency, providing some context, but lacks clear exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide. While annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, the description adds important details about data freshness ('updates daily'), the specific return format (Katzilla envelope with quality scores and citation data), and audit capabilities (SHA-256 data hash). This provides richer behavioral understanding than annotations alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences that each serve distinct purposes: stating the core function, specifying the data source and freshness, and describing the return format. There's no wasted language, and the most important information (what the tool does) comes first, followed by supporting details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has comprehensive annotations, 100% schema coverage, and an output schema exists, the description provides excellent contextual completeness. It covers the tool's purpose, data source characteristics, return format with quality metrics, and audit capabilities. The combination of description plus structured fields gives the agent everything needed to understand and use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (name filter and limit). The description doesn't add any additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate since the schema does the heavy lifting for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Search for') and resource ('members of the Brazilian Chamber of Deputies'), and distinguishes it from siblings by specifying the exact data source (Camara dos Deputados Open Data). It also explicitly describes the return format (Katzilla envelope), which helps differentiate it from other government tools that might return different data structures.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context by mentioning the data source and update frequency ('updates daily'), but doesn't explicitly state when to use this tool versus alternatives. While it's clearly for searching Brazilian deputies, there's no guidance about when to choose this over other government search tools or what specific use cases it serves best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and behavior. The description adds valuable context beyond annotations: it specifies the data source (open.canada.ca), license (Open Government Licence - Canada), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details (dataset count, topics, source, license, updates) and return format. Every sentence adds value—no wasted words—and it's structured efficiently in a single paragraph.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with filtering), rich annotations (read-only, idempotent, open-world), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, license, updates, and return structure, compensating well for any gaps. No additional explanation of return values is needed due to the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for 'query' (search query), 'organization' (filter by organization), and 'limit' (max results). The description does not add parameter-specific details beyond what the schema provides, such as examples for 'organization' beyond the schema's 'statcan, nrcan, hc-sc'. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search the Canadian government open data portal (open.canada.ca).' It specifies the resource (90,000+ datasets from federal departments) and scope (economics, health, environment, etc.), distinguishing it from sibling tools like government__data-australia or government__data-uk by focusing on Canada-specific data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching Canadian open data but does not explicitly state when to use this tool versus alternatives (e.g., other government data tools like government__data-australia). It mentions the source and update frequency, which provides some context, but lacks explicit guidance on exclusions or comparisons to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the data source ('Federal Register (Public Domain)'), update frequency ('updates daily'), and return format ('Katzilla envelope { data, quality, citation }') with details on quality scores and citation components. This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details in a logical flow (filtering, source, updates, return format). Every sentence adds value: the first defines the tool, the second adds filtering context, the third specifies source and frequency, and the fourth explains the output structure. There is no wasted verbiage, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, search functionality), rich annotations (covering read-only, non-destructive, idempotent, open-world traits), and the presence of an output schema (implied by the description of the return format), the description is complete enough. It covers purpose, source, update frequency, and output structure, compensating well for any gaps, and no critical information is missing for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., query for search terms, date ranges for filtering, docType with enum values). The description adds marginal value by mentioning 'date range filtering for historical regulatory data' and the types of documents, but does not provide additional syntax or format details beyond what the schema already covers. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), the resource ('U.S. Federal Register'), and the scope ('rules, proposed rules, notices, and presidential documents'), which is specific and distinguishes it from sibling tools like 'government__govinfo-search' or 'government__regulations-gov' that target different government data sources. It avoids tautology by not merely restating the name or title.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for historical regulatory data with date range filtering, but does not explicitly state when to use this tool versus alternatives (e.g., 'government__govinfo-search' for broader government documents or 'government__regulations-gov' for regulatory comments). It provides some context but lacks clear exclusions or named alternatives, leaving room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it discloses the source (Regulations.gov, Public Domain), update frequency (daily), and the return format (Katzilla envelope with quality scores and citation details including SHA-256 hash for audit). This enhances understanding of data freshness, reliability, and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, and the second details the source, updates, and return format. Every sentence adds essential information with zero waste, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with filtering), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers purpose, source, update frequency, and return structure, addressing key contextual needs without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the three parameters (query, limit, documentType). The description does not add any parameter-specific semantics beyond what the schema provides, such as examples or usage tips. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search Regulations.gov'), resource ('federal regulatory documents'), and scope ('including rules, proposed rules, notices, and public submissions'), distinguishing it from sibling tools by specifying the unique Regulations.gov source and document types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching federal regulatory documents but does not explicitly state when to use this tool versus alternatives (e.g., other government data tools like federal_register or govinfo). It provides context about the source but lacks explicit guidance on exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope'), explains quality scoring ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'), which helps the agent understand output behavior and data provenance.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by filtering details, source information, and return format explanation in a logical flow. Each sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, annotations, output schema), the description is complete: it covers purpose, filtering, data source, update frequency, and detailed return format. With annotations providing safety hints and an output schema presumably detailing the Katzilla envelope, no critical gaps remain, making it fully adequate for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter well-documented in the schema (e.g., query, forms, dates, pagination). The description mentions filtering by 'keyword, form type, and date range,' which aligns with parameters but does not add significant semantic details beyond the schema. Baseline 3 is appropriate as the schema handles most parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('Search'), resource ('SEC EDGAR for company filings'), and scope ('10-K, 10-Q, 8-K, etc.'), making the purpose specific and clear. It distinguishes from siblings by focusing on SEC filings, unlike other government tools (e.g., congress-bills, federal-register), and the title 'SEC Edgar' reinforces this distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by listing filterable fields (keyword, form type, date range) and mentions the data source and update frequency, which suggests when to use it for current SEC filings. However, it does not explicitly state when not to use it or name alternative tools for similar data, leaving some ambiguity about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it specifies the data source, update frequency ('updates daily'), and details the return format ('Katzilla envelope { data, quality, citation }') with quality metrics and citation information. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with two sentences that efficiently convey purpose, data details, and return format. Every sentence adds value: the first explains what the tool does and the data structure, and the second covers source, updates, and output envelope. There is no wasted text, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple read-only query), rich annotations (covering safety and behavior), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the tool's function, data source, update frequency, and output structure, leaving no significant gaps for an AI agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'limit' parameter fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, such as default usage or examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Current travel advisories from the U.S. Department of State. Returns advisory level (1-4), country, and description.' It specifies the verb ('returns'), resource ('travel advisories'), and distinguishes itself from siblings by focusing on government travel data rather than agriculture, consumer, or other domains. The explanation of advisory levels adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source and update frequency ('Source: U.S. Department of State (Public Domain), updates daily'), but it does not explicitly state when to use this tool versus alternatives or any prerequisites. Among siblings, it's clear this is for government travel data, but no direct comparisons or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the data source (U.S. Geological Survey), update frequency ('real-time'), and output structure ('Katzilla envelope' with quality scores and citation details including SHA-256 hash). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence. Every sentence adds value: the first defines the tool's function, and the second provides source, update frequency, and output format details. It is efficiently structured with zero wasted words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, 100% schema coverage, annotations, and an output schema), the description is complete enough. It covers purpose, source, real-time updates, and output structure, complementing the structured fields. With annotations handling safety and an output schema likely detailing the 'Katzilla envelope', no critical gaps remain for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain magnitude scales or time formats). With high schema coverage, the baseline score of 3 is appropriate, as the description relies on the schema for parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Query recent earthquake events from the USGS Earthquake Hazards Program' with specific resources (magnitude, location, time, coordinates) and distinguishes it from sibling tools by specifying the data source (USGS) and domain (hazards). It uses a clear verb ('Query') and resource ('earthquake events'), making it distinct from other hazard tools like 'fema-disasters' or 'nasa-wildfires'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'recent earthquake events' and the source (USGS), but does not explicitly state when to use this tool versus alternatives. It lacks guidance on prerequisites, exclusions, or comparisons to sibling tools, leaving the agent to infer usage based on the domain and data type without clear directives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the daily update schedule, specifies the return format (Katzilla envelope with data/quality/citation), and explains quality metrics (freshness/uptime/confidence) and citation details (source URL, license, SHA-256 hash). While annotations cover read-only/non-destructive/idempotent/open-world aspects, the description enriches understanding of data characteristics and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first establishes purpose and source, the second details the return format and its components. Every element serves a clear informational purpose with zero redundant or wasted content, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), complete parameter schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers data source, update frequency, return format, and quality/citation details—effectively supplementing the structured metadata without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents the single 'limit' parameter. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 where structured data carries the parameter documentation burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific purpose: retrieving COVID-19 case surveillance data from the CDC. It specifies the data source (CDC Public Domain), update frequency (daily), and distinguishes itself from siblings by focusing on COVID-19 data rather than other health datasets like CDC WONDER or NIH clinical trials.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for COVID-19 data needs but provides no explicit guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like health__cdc-wonder or differentiate use cases, leaving the agent to infer appropriate contexts without clear boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source ('NIH RePORTER'), update frequency ('updates daily'), and return format ('Katzilla envelope { data, quality, citation }') with details on quality scores and citation components. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by return details and source information in two efficient sentences. Every sentence adds value: the first defines the search action and scope, the second explains the return format and data provenance. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, return format, data source, and update frequency, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents parameters (query, limit, fiscal_year). The description does not add additional semantic details beyond the schema, such as examples or advanced usage tips. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), resource ('NIH Research Portfolio Online Reporting Tools (RePORTER) for funded research projects'), and scope ('by keyword'), distinguishing it from sibling tools like 'health__nih-clinical-trials' or 'science__nsf-awards' by focusing on NIH-funded research projects with specific return fields. It provides a clear, specific purpose without tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching NIH-funded research projects by keyword, but does not explicitly state when to use this tool versus alternatives (e.g., other health or science tools in the sibling list). There is no guidance on exclusions or prerequisites, leaving usage context somewhat open-ended.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source ('Eurostat (Open Data)'), update frequency ('updates monthly'), and detailed return structure ('Katzilla envelope { data, quality, citation }') with quality metrics and citation details. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and updates; the second details the return format. Every sentence adds critical information (e.g., return envelope structure, quality scores, citation details) with zero waste, making it front-loaded and highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (statistical querying), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return format in detail, compensating for any gaps. The output schema likely defines the return structure, so the description's elaboration on the 'Katzilla envelope' provides helpful context without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('geo' and 'year') fully documented in the schema. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain 'geo' codes or 'year' formatting further). Thus, it meets the baseline of 3 where the schema handles parameter semantics adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Query immigration statistics'), resource ('from Eurostat'), and scope ('data on immigration by age and sex for EU member states'). It distinguishes this tool from siblings like 'demographics__eurostat' by specifying the migration domain, making the purpose unambiguous and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source ('Eurostat (Open Data)') and update frequency ('updates monthly'), but it does not explicitly state when to use this tool versus alternatives (e.g., other Eurostat tools like 'economic__eurostat-gdp' or general demographic tools). No exclusions or prerequisites are provided, leaving usage guidance at an implied level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it discloses the source (7Timer Free API), update frequency (monthly), and the return structure (Katzilla envelope with data, quality, citation details including SHA-256 hash). This enriches understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by source details and return structure. Every sentence adds value: the first defines the tool, the second provides source context, and the third explains the output format. There is no wasted text, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return structure, which, combined with structured fields, provides sufficient context for an agent to use the tool effectively without needing additional explanation of parameters or outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for lat, lon, and product (including enum values). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining the implications of product choices (e.g., astro vs. civil). Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with a specific verb ('Get weather forecasts') and resource ('from 7Timer for a given location'), clearly distinguishing it from sibling tools that focus on agriculture, consumer data, crime, crypto, etc. It also specifies the data source (7Timer Free API) and update frequency (monthly), making the purpose highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for weather forecasting needs, but does not explicitly state when to use this tool versus alternatives (e.g., other weather-related tools like environment__canada-weather or environment__openmeteo-aq). It provides context about the source and update frequency, but lacks explicit guidance on when-not-to-use or named alternatives, leaving usage somewhat implied rather than clearly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint, covering core safety and behavior. The description adds valuable context beyond annotations: it discloses the return format (Katzilla envelope), data quality metrics (freshness/uptime/confidence), citation details (source URL, license, SHA-256 hash), and update frequency (monthly). This significantly enhances behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, the second details the return format and quality metrics. Every element adds value without redundancy, and key information (like the Katzilla envelope) is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with quality metrics), rich annotations (covering safety and behavior), and the presence of an output schema (implied by 'Has output schema: true'), the description is highly complete. It explains the unique return format, data quality aspects, and source characteristics, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters clearly documented in the schema. The description does not add any parameter-specific details beyond what the schema provides. It mentions search functionality generally but offers no additional syntax, format, or constraint information for the query or limit parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Internet Archive'), resources targeted ('books, media, and web archives'), and distinguishes it from siblings by specifying the data source and return format. It goes beyond a generic search tool by mentioning the Katzilla envelope structure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching Internet Archive content but provides no explicit guidance on when to use this tool versus alternatives. It mentions the source and update frequency, which gives some context, but lacks clear when/when-not statements or named alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the source (Lorem Picsum Free API), update frequency (monthly), and details about the return format (Katzilla envelope with quality scores, citation, and SHA-256 hash). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential details (source, updates, return format). Every sentence adds value—none are redundant or wasteful—making it efficiently structured and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no nested objects), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the description of return format), the description is complete. It covers purpose, source, update frequency, and return structure, leaving no significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for both parameters (page and limit). The description does not add any additional meaning or details about the parameters beyond what the schema provides, such as default values or usage context. Thus, it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('List') and resource ('placeholder images from Lorem Picsum'), making the purpose specific. It distinguishes from siblings by specifying the data source (Lorem Picsum) and return format (Katzilla envelope), which none of the other tools mention, ensuring clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching placeholder images from a specific API (Lorem Picsum) and notes the update frequency (monthly), but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., other image or data tools in the sibling list). No exclusions or direct comparisons are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it discloses the source (Shields.io, CC0), update frequency (monthly), and the specific return structure (Katzilla envelope with data, quality, citation components including freshness/uptime/confidence scores and audit hash). This enriches the agent's understanding of the tool's behavior and output characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded: the first sentence states the core purpose, followed by essential behavioral details (source, updates, return format). Every sentence adds critical information (output structure, quality metrics, citation details) with zero waste. It efficiently communicates necessary context in a compact form.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, annotations, output schema), the description is complete. It covers purpose, source, update cadence, and detailed return structure. With annotations covering safety/idempotency and an output schema presumably detailing the Katzilla envelope, the description fills gaps by explaining the envelope's components (quality scores, citation details), making it fully adequate for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all three parameters (label, message, color) well-documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., no examples, constraints, or usage tips). Baseline score of 3 is appropriate since the schema fully covers parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate a Shields.io badge in JSON format.' It specifies the exact resource (Shields.io badge) and output format (JSON), and distinguishes from siblings by being the only badge-generation tool in the international category. The mention of 'International Data Agent' and source attribution adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for generating badges with the Katzilla envelope format, but does not explicitly state when to use this tool versus alternatives. No sibling tools are directly comparable (e.g., other badge generators), so there's no explicit guidance on alternatives or exclusions. Usage context is implied through the output format and source details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source (Squiggle API), update frequency (monthly), return format (Katzilla envelope with quality scores and citation details), and audit features (SHA-256 hash). While annotations cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details that help the agent understand what to expect from the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format and its components. Every sentence adds value without redundancy, and key information (data source, return format) is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, 100% schema coverage), rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema (implied by context signals), the description is complete. It covers purpose, source, update frequency, and return format, providing sufficient context for the agent to use the tool effectively without needing to explain parameters or output details that are already documented elsewhere.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage with a clear enum for the 'query' parameter. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain what 'teams', 'games', or 'tips' data types entail). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get AFL data from Squiggle') and resource ('Australian Football League data'), and distinguishes it from siblings by specifying the data source (Squiggle API) and return format (Katzilla envelope). It goes beyond the title/name to explain what data is retrieved and from where.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source (Squiggle) and update frequency (monthly), but does not explicitly state when to use this tool versus alternatives or provide any exclusions. It lacks guidance on when this specific AFL data tool should be chosen over other sports or data tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the data source ('data.europa.eu'), update frequency ('daily'), and details the return format ('Katzilla envelope { data, quality, citation }') with quality metrics and citation info, which is not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format specifics. Every sentence adds value: the first states the action, the second provides source and update info, and the third explains the output structure concisely without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (covering safety and behavior), and the presence of an output schema, the description is complete. It adds necessary context like source, update frequency, and return format details, which complements the structured data effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for 'query' and 'limit' parameters. The description does not add any additional meaning or examples for these parameters beyond what the schema provides, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Search the European Union open data portal for datasets.' It specifies the verb ('search'), resource ('datasets'), and source ('data.europa.eu'), distinguishing it from sibling tools like 'meta__datagov-catalog' or other domain-specific tools by focusing on EU open data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'Search the European Union open data portal for datasets' and 'updates daily,' but it does not explicitly state when to use this tool versus alternatives (e.g., other data catalog tools in the list). No exclusions or specific prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it discloses the data source ('Nobel Prize API (CC0)'), update frequency ('updates daily'), and return structure ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation contents. This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source and return details. Every sentence adds value: the first states the query action, the second provides source and update info, and the third explains the return structure. It is efficiently structured with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, source, update frequency, and return format, which complements the structured data. No additional explanation of return values is needed due to the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (limit, year, category). The description mentions filtering by year and category but does not add significant semantic details beyond what the schema provides (e.g., it doesn't explain category codes like 'phy' or 'che' in more depth). Baseline 3 is appropriate as the schema handles most parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query Nobel Prize data by year and category. Returns laureates and prize details.' It specifies the verb ('query'), resource ('Nobel Prize data'), and distinguishes it from siblings by focusing on Nobel Prize data specifically, which is unique among the listed tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning filtering by year and category, but does not explicitly state when to use this tool versus alternatives (e.g., other science tools like arXiv or PubMed). It provides some context ('Science & Research Agent') but lacks explicit guidance on exclusions or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable context beyond annotations: it discloses the data source (Sunrise-Sunset API), update frequency (daily), and output structure (Katzilla envelope with quality scores and citation details). Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, so the description appropriately supplements with operational details without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source and output details in two efficient sentences. Every element (e.g., API source, update frequency, return format) adds value without redundancy, making it compact and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, rich annotations (read-only, idempotent, etc.), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, source, update behavior, and output format, providing sufficient context for an agent to use the tool effectively without needing to explain return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents parameters (lat, lng, date). The description adds minimal semantics by mentioning 'latitude/longitude and date' but does not provide additional details like format examples or constraints beyond what the schema states. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with specific verbs ('Get sunrise, sunset, and twilight times') and resources ('for a given latitude/longitude and date'), clearly distinguishing it from sibling tools like weather or environmental APIs. It identifies the source (Sunrise-Sunset API) and output format (Katzilla envelope), making the purpose unambiguous and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying the required inputs (latitude/longitude and date) and the daily update frequency, but it does not explicitly state when to use this tool versus alternatives (e.g., weather APIs for broader data or other science tools). No exclusions or clear alternatives are mentioned, leaving some ambiguity in context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations by specifying the data source, update frequency ('updates daily'), and detailed return structure ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation contents. No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and scope, the second details return format and data characteristics. Every element serves a purpose with zero wasted words, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, 100% schema coverage, and existence of an output schema, the description provides complete context. It explains what the tool does, data source, update frequency, and return structure in detail, compensating adequately where structured fields might not convey all necessary information for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description adds marginal value by mentioning the entity types ('country, region, or ASN') and time range concepts, but doesn't provide additional syntax, format details, or constraints beyond what the schema provides. Baseline 3 is appropriate given complete schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query Internet Outage Detection and Analysis (IODA) for internet connectivity signals') and resources ('by country, region, or ASN'), distinguishing it from sibling tools which cover unrelated domains like agriculture, consumer, crypto, etc. It explicitly identifies the data source and type of analysis performed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying the data source ('IODA (Georgia Tech)') and update frequency ('updates daily'), but provides no explicit guidance on when to use this tool versus alternatives or any prerequisites. It doesn't mention sibling tools or competing data sources for similar information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (World Bank), update frequency (daily), return format (Katzilla envelope with data, quality, citation), and audit details (SHA-256 hash), enhancing transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format, all in two efficient sentences. Every sentence adds value: the first defines the query scope, and the second clarifies data provenance and output structure, with zero redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (query with pagination), rich annotations (read-only, idempotent), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, updates, and output details, leaving no gaps for the agent to understand tool behavior without needing further explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed descriptions for all parameters (indicator, year, page, limit). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining indicator meanings or pagination behavior. Baseline 3 is appropriate since the schema fully documents parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb 'Query' and the resource 'World Bank Worldwide Governance Indicators', listing all six specific indicators (Voice & Accountability, Political Stability, etc.). It distinguishes from sibling tools by focusing on governance indicators, whereas siblings like economic__world-bank cover broader economic data, making the purpose highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for querying governance indicators, but does not explicitly state when to use this tool versus alternatives (e.g., other security or economic tools). It mentions the source and update frequency, which provides some context, but lacks clear guidance on scenarios or exclusions for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. While annotations indicate read-only, non-destructive, idempotent, and open-world traits, the description specifies the return format (Katzilla envelope with data, quality, citation), quality scoring (freshness/uptime/confidence), citation details (source URL, license, SHA-256 hash), and update frequency (monthly). This enriches the agent's understanding of output behavior and data provenance, though it could mention rate limits or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details (data types, source, updates, return format) in a logical flow. Every sentence adds value: the second sentence elaborates on data scope, the third on source and frequency, and the fourth on output structure. It is efficiently structured with zero redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (EU transport statistics), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete enough. It covers purpose, data scope, source, update frequency, and detailed return behavior (including quality metrics and citation), which complements the structured fields. No significant gaps remain for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents the two parameters (geo and dataset), including enums and defaults. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining dataset choices or geo code formats. It meets the baseline for high schema coverage but does not compensate with extra insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it retrieves EU transport statistics from Eurostat, specifying the data types (passenger and freight transport by mode), geographic scope (EU/EEA countries), and indicators (modal split, volume, safety). It distinguishes itself from siblings like 'transport__bc-ferries' or 'transport__bts-stats' by focusing on Eurostat's EU-wide transport datasets, making the verb+resource+scope specific and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source (Eurostat) and update frequency (monthly), but it does not explicitly state when to use this tool versus alternatives. For example, it does not compare with sibling tools like 'demographics__eurostat' or 'economic__eurostat-gdp', nor does it provide exclusions or prerequisites. The guidance is limited to the tool's scope without explicit alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it specifies the data source (NHTSA), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation metadata including SHA-256 hash). This enhances transparency about data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the tool's purpose and parameters, the second details the return format and data quality. Every sentence provides essential information with no wasted words, making it front-loaded and highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with three parameters), rich annotations, and the presence of an output schema, the description is complete. It covers the tool's purpose, data source, update frequency, and return format details, which complements the structured fields effectively without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear parameter descriptions in the schema. The description mentions searching by make, model, and year, which aligns with the schema but does not add significant semantic value beyond what the schema already provides. The baseline score of 3 is appropriate given the comprehensive schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches U.S. vehicle safety recalls from NHTSA by make, model, and year. It specifies the exact resource (NHTSA recalls) and distinguishes it from sibling tools like nhtsa-complaints and nhtsa-safety-ratings by focusing on recalls rather than complaints or ratings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when searching for recalls by vehicle attributes, but does not explicitly state when to use this tool versus alternatives like nhtsa-complaints or nhtsa-safety-ratings. It mentions the data source and update frequency, which provides some context, but lacks explicit guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it discloses the return format ('Katzilla envelope { data, quality, citation }'), explains quality scoring ('freshness/uptime/confidence'), and describes citation details including audit features. No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, parameters, source, and update frequency; the second explains the return format and its components. Every element adds value without redundancy, making it front-loaded and zero-waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), 100% schema coverage, and the presence of an output schema (implied by the return format description), the description is complete. It adds necessary context like data source, update cadence, and return structure without needing to repeat what's already in structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all three parameters. The description mentions filtering 'by year, report, and state' but doesn't add syntax, format, or semantic details beyond what the schema provides (e.g., it doesn't explain report options like 'crop_production_practices' or state abbreviation rules). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Query the USDA ARMS API'), resource ('farm business economics data'), and scope ('by year, report, and state'). It distinguishes from sibling tools like agriculture__usda-nass and agriculture__usda-fooddata by specifying the ARMS survey focus on farm economics rather than other agricultural data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('for farm business economics data') and mentions the data source and update frequency ('USDA ERS, updates monthly'). However, it doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools, though the specificity helps differentiate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source (USDA FoodData Central), update frequency (monthly), and details about the return format (Katzilla envelope with quality scores and citation), which helps the agent understand data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers purpose, source, update frequency, and return structure, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema fully documents the 'query' and 'limit' parameters. The description does not add any parameter-specific details beyond what the schema provides, such as query examples or limit constraints, so it meets the baseline for adequate coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search'), resource ('USDA FoodData Central database'), and content ('food and nutrient information'), distinguishing it from sibling tools like agriculture__usda-ers or agriculture__usda-nass by focusing on food data rather than economic or agricultural statistics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It implicitly suggests usage for food and nutrient queries, with context about the data source and update frequency, but does not explicitly state when to use alternatives or exclude specific scenarios, such as non-food searches or real-time data needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits. The description supplements this by specifying the data source (USDA NASS, Public Domain), update frequency (monthly), and the return format (Katzilla envelope with data, quality scores, and citation details including a SHA-256 hash for audit). This enriches the agent's understanding of data freshness, licensing, and output structure, though it doesn't cover rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded. The first sentence clearly states the tool's purpose and key parameters. The second sentence efficiently adds source, update frequency, and output format details. Every sentence earns its place with no wasted words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, annotations, and an output schema), the description is complete enough. It covers purpose, source, update frequency, and output structure. With annotations providing safety and idempotency hints, and an output schema presumably detailing the Katzilla envelope, the description fills in necessary contextual gaps without redundancy. It adequately prepares an agent for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so all parameters are well-documented in the schema itself. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it mentions 'by commodity, year, and state' but the schema already details these). With high schema coverage, the baseline is 3, as the description doesn't compensate with extra param info but doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query the USDA National Agricultural Statistics Service (NASS) Quick Stats API for crop and livestock statistics by commodity, year, and state.' It specifies the exact resource (USDA NASS Quick Stats API), the action (query), and the data domain (crop and livestock statistics). This distinguishes it from sibling tools like agriculture__usda-ers or agriculture__usda-fooddata, which target different USDA data sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for querying USDA NASS agricultural statistics. It implicitly suggests alternatives by mentioning the source (USDA NASS) and data type (crop and livestock statistics), but does not explicitly state when not to use it or name specific alternative tools for similar queries. The context is sufficient for an agent to infer usage, but lacks explicit exclusions or direct sibling comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source (CFPB, Public Domain), update frequency ('updates daily'), and the return format ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation components. This enhances the agent's understanding of data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a structured manner. Every sentence adds value: coverage details, filtering support, data volume, source information, and return format. There is no redundant or wasted text, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, search functionality) and the presence of annotations and an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, data scope, usage context, behavioral traits, and return structure, leaving no significant gaps for the agent to understand how to invoke and interpret results effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 8 parameters. The description mentions 'date range filtering and pagination', which aligns with parameters like dateReceivedMin, dateReceivedMax, page, and limit, but does not add new semantic details beyond what the schema provides. It also lists example product types, which are covered by the schema's description. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), the resource ('consumer complaints about financial products from the Consumer Financial Protection Bureau'), and the scope ('Covers mortgages, credit cards, student loans, debt collection, and more'). It clearly distinguishes this tool from its sibling consumer__cfpb-hmda, which handles different CFPB data (HMDA vs. complaints).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching consumer complaints with filtering capabilities. It mentions date range filtering and pagination, which are key usage scenarios. However, it does not explicitly state when not to use it or name specific alternatives among the many sibling tools, such as consumer__cpsc-recalls for product safety issues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope { data, quality, citation }'), describes quality metrics ('freshness/uptime/confidence'), and details citation contents ('source URL, license, SHA-256 data hash'), which aids in understanding output behavior and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, and source; the second details the return format and metadata. Every sentence adds value without redundancy, and it is front-loaded with key information. No wasted words or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters with full schema coverage, annotations covering safety/idempotency, and an output schema implied by the return format description), the description is complete. It explains the data source, update frequency, return structure, and quality metrics, compensating for any gaps. With annotations and schema handling technical details, the description provides sufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter well-documented in the schema (year range, state codes, actionsTaken enum). The description mentions parameters implicitly ('by action type, year, and geography') but does not add significant semantic details beyond what the schema provides, such as explaining the significance of action types or geographic scope. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('aggregated mortgage lending statistics') and resources ('Home Mortgage Disclosure Act data from CFPB'), distinguishing it from siblings like consumer__cfpb-complaints by focusing on mortgage data rather than complaints. It explicitly mentions the data scope ('loan originations, denials, and applications nationwide') and source details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('aggregated mortgage lending statistics by action type, year, and geography'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for HMDA data queries without contrasting with other consumer tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it discloses the data source ('U.S. Consumer Product Safety Commission (Public Domain)'), update frequency ('updates daily'), and detailed return format ('Katzilla envelope { data, quality, citation }' with quality metrics and citation details). This significantly enhances behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and filtering, the second provides source metadata and detailed return format. Every element adds value - no wasted words. It's appropriately front-loaded with the core functionality followed by important behavioral details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with filtering), rich annotations (four behavioral hints), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, source, update frequency, and detailed return format - everything needed beyond what structured fields already provide. The output schema existence means the description doesn't need to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all four parameters well-documented in the input schema. The description mentions filtering 'by date range and product type' which aligns with startDate, endDate, and productType parameters, but adds no additional semantic context beyond what the schema already provides. The limit parameter isn't mentioned in the description at all. Baseline 3 is appropriate given complete schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Search'), resource ('U.S. Consumer Product Safety Commission (CPSC) product recall data'), and scope ('Filter by date range and product type'). It distinguishes from sibling tools like 'consumer__cpsc-violations' and 'health__fda-recalls' by specifying CPSC as the source agency and product recalls as the focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search U.S. Consumer Product Safety Commission (CPSC) product recall data') and mentions filtering capabilities. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, though the CPSC focus implicitly distinguishes it from other recall tools like FDA or NHTSA recalls.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source ('U.S. Consumer Product Safety Commission'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation components. This enriches understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: it states the purpose, provides source and update info, and explains the return format. Each sentence adds essential information without redundancy, making it front-loaded and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, leaving detailed output to the schema. No gaps are evident for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting the 'query' and 'limit' parameters. The description does not add any additional meaning or examples beyond what the schema provides (e.g., it doesn't elaborate on search syntax or result ordering). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search CPSC product safety violations and section 15 reports for hazardous consumer products.' It specifies the verb ('Search'), resource ('CPSC product safety violations and section 15 reports'), and distinguishes it from sibling tools like 'consumer__cpsc-recalls' by focusing on violations and reports rather than recalls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: searching for hazardous consumer products via CPSC data. It mentions the source and update frequency ('updates daily'), which aids in timing decisions. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., 'consumer__cpsc-recalls'), missing full differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it specifies the data source (Federal Trade Commission), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation info), which helps the agent understand data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format explanation. Every sentence adds value: the first defines the tool, the second gives source context, and the third clarifies the output structure, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations (read-only, idempotent, open world), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers purpose, source, updates, and output structure, leaving no significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'limit' parameter fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without extra param semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('Query'), resource ('FTC Do Not Call (DNC) complaint data'), and scope ('reported unwanted call complaints filed with the Federal Trade Commission'), making the purpose specific and clear. It distinguishes from sibling tools like consumer__cfpb-complaints by specifying the FTC DNC dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: querying FTC DNC complaint data, with source and update frequency mentioned. However, it does not explicitly state when not to use it or name alternatives among siblings, such as consumer__cfpb-complaints for other consumer complaint data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source ('Federal Trade Commission (Public Domain)'), update frequency ('updates daily'), and return format ('Katzilla envelope { data, quality, citation }' with details on quality scoring and citation contents). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description complements them well without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: purpose, source/update info, and return format. Each sentence adds essential information with zero waste, making it easy to parse and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations (read-only, non-destructive, idempotent, open-world), and existence of an output schema, the description is complete. It covers purpose, source, update frequency, and return structure, leaving detailed parameter and output specifics to the schemas.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage (one parameter 'limit' fully documented), so the baseline is 3. The description does not add any parameter-specific semantics beyond what the schema provides, but it implies the tool returns early termination notices without specifying how 'limit' interacts with that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query FTC Hart-Scott-Rodino early termination notices') and resources ('merger and acquisition filings'), and distinguishes it from siblings by specifying it shows 'companies that received early termination of the HSR waiting period' rather than other FTC or consumer data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Query FTC Hart-Scott-Rodino early termination notices for merger and acquisition filings'), but does not explicitly state when not to use it or name specific alternatives among the many sibling tools, though the specificity implies it's for FTC merger data only.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (CourtListener/Free Law Project), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). This provides important behavioral insights about data freshness, auditability, and response structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the tool's purpose and scope, the second details the return format and data quality. Every element adds value—source information, update frequency, and return structure—with zero wasted words. It's appropriately front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters), rich annotations covering safety and idempotency, and the existence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return format details. The combination of description, annotations, and schema provides comprehensive context for the agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (query and limit) well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions searching but doesn't provide additional guidance on query formulation or limit usage. Given the high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search U.S. court opinions from CourtListener (Free Law Project). Access federal and state court decisions.' It specifies the verb ('search'), resource ('U.S. court opinions'), and scope ('federal and state court decisions'), distinguishing it from sibling tools like crime__fbi-most-wanted or crime__recap-docket which focus on different legal data sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching U.S. court opinions with daily updates from CourtListener. It mentions the source and update frequency, which helps determine applicability. However, it doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools, though the crime__recap-* tools are implied alternatives for different legal data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints, but the description adds valuable context beyond this: it specifies the data source (Coinpaprika Free API), update frequency (real-time), and the return format (Katzilla envelope with quality scores and citation details), which are not captured in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format. Every sentence provides essential information without redundancy, making it front-loaded and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, source, update frequency, and return structure, leaving no significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'coinId' fully documented in the schema. The description does not add any additional meaning or examples beyond what the schema provides, such as more coin ID examples or clarification on global market data behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get cryptocurrency market data') and resources ('from Coinpaprika'), and distinguishes it from siblings by specifying the data source (Coinpaprika) and data types (prices, volume, market cap), unlike other crypto tools like crypto__coinranking or crypto__blockchain-stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Get cryptocurrency market data... updates real-time') and implies when to use it (for Coinpaprika-sourced data), but does not explicitly state when not to use it or name specific alternatives among the many sibling crypto tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description adds valuable behavioral context beyond that: it specifies the data source (CoinRanking, Public), update frequency (real-time), and return format (Katzilla envelope with quality scores and citation details including SHA-256 hash for audit). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with core functionality, uses efficient sentences without waste, and structures information logically (purpose, features, source, return format). Every sentence adds value, such as specifying the Katzilla envelope details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, real-time data), rich annotations, and the presence of an output schema (implied by mention of return format), the description is complete. It covers purpose, usage, behavioral traits, source, and output structure, leaving no significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds minimal parameter semantics by mentioning filtering by tier and sorting options, but this is largely redundant with the schema. Baseline 3 is appropriate as the schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get live cryptocurrency prices and market data') and resources ('for 2,000+ coins from CoinRanking'), distinguishing it from sibling tools like crypto__coinpaprika or crypto__coinlore-stats by specifying the data source and scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Filter by tier, sort by market cap, price, or 24h change') and implies when to use it for real-time cryptocurrency data. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as crypto__blockchain-stats for different data types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies that the data is free, real-time, and requires no API key, and details the return format (Katzilla envelope with quality scores and citation for audit). Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, so the description complements this with practical usage details without contradiction, earning a high score for transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by key details like supported chains, no API key, source, and return format. Each sentence adds essential information without redundancy, making it efficient and well-structured for quick understanding by an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, real-time data across multiple chains), the description is complete: it covers the purpose, data source, key features (free, no API key), supported chains, return format, and auditability. With annotations providing safety hints and an output schema existing (though not detailed here), the description adequately supplements the structured data without needing to explain return values, ensuring the agent has sufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents all 6 parameters, including enums and defaults. The description does not add parameter-specific semantics beyond what the schema provides, such as explaining how 'networkId' maps to chains or 'action' affects outputs. However, it implies parameter usage by mentioning data types like pools and token details, aligning with the baseline score when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: fetching free decentralized exchange (DEX) and DeFi data from DexPaprika, with specific examples like liquidity pools, token prices, OHLCV charts, and 24h trade volume. It clearly distinguishes from sibling tools by specifying the data source (DexPaprika) and the types of cryptocurrency/blockchain data it provides, which is unique among the listed crypto siblings (e.g., coinpaprika, etherscan, blockchain-stats).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for real-time DEX/DeFi data across multiple chains, with no API key required. It implies usage by mentioning the supported chains (Ethereum, Solana, etc.) and data types, but does not explicitly state when not to use it or name specific alternatives among siblings, such as using crypto__coinpaprika for broader cryptocurrency data instead of DEX-specific data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it discloses the data source (Etherscan with terms reference), update frequency (real-time), and detailed return format (Katzilla envelope with quality scores and citation details including SHA-256 hash for audit). This enriches the agent's understanding of reliability and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: it front-loads the core purpose in the first sentence, followed by supporting details (chain support, source, updates, return format) in a logical flow. Every sentence adds value without redundancy, making it efficient for an agent to parse and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters), rich annotations (covering read-only, non-destructive, idempotent, open-world traits), and the presence of an output schema (implied by the detailed return format description), the description is complete. It adequately explains the tool's purpose, behavior, and output without needing to reiterate structured data, providing all necessary context for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (address and chainId) well-documented in the schema, including patterns, defaults, and examples. The description adds minimal parameter semantics beyond the schema, only implying that chain ID enables multi-chain support. Since the schema carries the heavy lifting, the baseline score of 3 is appropriate, as the description does not significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get the ETH balance'), target resource ('Ethereum address'), and method ('using Etherscan'), with explicit mention of multi-chain support via chain ID. It distinguishes itself from sibling tools like crypto__etherscan-gas, crypto__etherscan-price, and crypto__etherscan-txlist by focusing solely on balance retrieval rather than gas fees, prices, or transaction lists.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it specifies when to use this tool (to get ETH balance of an Ethereum address) and mentions support for multiple EVM chains via chain ID. However, it does not explicitly state when NOT to use it or name alternatives (e.g., other balance-checking tools), though the sibling list shows related Etherscan tools for different purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (Etherscan with terms reference), update frequency (real-time), and output format (Katzilla envelope with quality scores and citation details including SHA-256 hash for audit). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence. Every sentence adds value: the first defines the tool's function and returned data, and the second specifies source, updates, and output structure. There is no wasted text, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), 100% schema coverage, and presence of an output schema (implied by mention of Katzilla envelope), the description is complete. It adequately explains what the tool does, its source, update behavior, and output format, leaving no significant gaps for an agent to understand and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter (address, limit, chainId) well-documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as explaining chainId options beyond the schema's examples. However, it implies address usage and real-time data, which aligns with but does not extend schema information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get recent transactions'), resource ('for an Ethereum address'), and data source ('using Etherscan'), with explicit mention of returned fields like transaction hash, from, to, value, timestamp, and gas used. It distinguishes itself from sibling tools like crypto__etherscan-balance or crypto__etherscan-gas by focusing on transaction lists rather than balances, gas prices, or other metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to retrieve recent Ethereum transactions for a given address, with real-time updates from Etherscan. It does not explicitly state when not to use it or name alternatives among siblings, but the context is sufficient for an agent to infer its purpose relative to other crypto tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), but the description adds valuable context beyond these. It discloses the source (Gemini Free API), real-time updates, and details about the return structure (Katzilla envelope with quality scores and citation for audit), which are not captured in annotations. No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded and efficiently structured in two sentences: the first states the purpose and source, and the second explains the return format and its components. Every sentence adds essential information without redundancy, making it highly concise and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, simple schema), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context about the data source, update frequency, and return structure, filling gaps not covered by structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the single parameter 'symbol' with examples. The description does not add any further parameter semantics beyond what the schema provides, such as format details or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description does not compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get'), resource ('cryptocurrency ticker data'), and source ('Gemini exchange'). It distinguishes from siblings by specifying the data source (Gemini) and the structured return format (Katzilla envelope), unlike generic crypto tools like crypto__coinpaprika or crypto__coinranking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it specifies the data source (Gemini exchange), indicates real-time updates, and mentions the return format. However, it does not explicitly state when not to use this tool or name alternatives among siblings (e.g., crypto__coinpaprika for broader data), so it lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true). The description adds valuable context beyond annotations: it specifies the data source (Art Institute of Chicago), update frequency ('updates daily'), licensing (CC0 1.0 for metadata), and details about the return envelope structure ('Katzilla envelope { data, quality, citation }') with quality scoring and citation components. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers the tool's purpose and return data, and the second adds source, licensing, update frequency, and envelope details. Every sentence provides essential information with zero waste, making it highly concise and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search functionality with metadata returns), the description is complete. It explains what the tool does, the data source, licensing, update frequency, and the structure of the return envelope. With annotations covering safety and idempotency, and an output schema presumably detailing the envelope structure, no critical information is missing for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for both parameters ('query' and 'limit'). The description does not add any parameter-specific semantics beyond what the schema already provides (e.g., it doesn't clarify search syntax or result ordering). Given the high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), the resource ('Art Institute of Chicago's collection of over 300,000 artworks'), and the return content ('artwork metadata including artist, date, medium, and image links'). It clearly distinguishes this from sibling tools by specifying the unique cultural dataset (AIC artworks), unlike other culture tools like 'culture__met-museum' or 'culture__smithsonian'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching AIC's artwork collection. However, it does not explicitly state when not to use it or name specific alternatives among the many sibling tools (e.g., 'culture__met-museum' for another museum's collection), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the source ('Colormind'), licensing ('Free / No restrictions'), update frequency ('updates daily'), and output structure ('Returns the Katzilla envelope { data, quality, citation }'). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description complements them without contradiction, earning a high score for added transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional details in a structured manner. Every sentence adds value: the first explains what the tool does, the second details output and source, and the third clarifies the return format. There is no wasted text, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter), rich annotations (read-only, non-destructive, idempotent, open-world), and the presence of an output schema, the description is complete. It covers purpose, source, licensing, update frequency, and output structure, providing all necessary context for an AI agent to use the tool effectively without needing to explain return values, which are handled by the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'model' parameter fully documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as examples of model usage or effects on output. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding but doesn't need to compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Generate harmonious color palettes using Colormind's deep-learning model. Returns a 5-color palette with RGB values and hex codes.' It specifies the verb ('Generate'), resource ('color palettes'), method ('Colormind's deep-learning model'), and output format, clearly distinguishing it from sibling tools which focus on different domains like agriculture, consumer data, or entertainment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning 'Source: Colormind (Free / No restrictions), updates daily,' indicating it's a free tool with daily updates, which helps in deciding when to use it. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as other color-related tools (none are listed in siblings), so it falls short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, but the description discloses that the source 'updates daily' (implying freshness considerations) and details the return format ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation contents. This enriches the agent's understanding of data reliability and output structure, though it doesn't cover rate limits or auth needs (not required here).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured and front-loaded: the first sentence states the core purpose, followed by category details, source information, and return format explanation. Every sentence adds value (e.g., source credibility, output structure), with no redundant or verbose content, making it highly concise and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), high schema coverage (100%), rich annotations (covering safety and idempotency), and presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context like source updates, return format details, and data quality aspects, ensuring the agent has sufficient information without over-explaining structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'category' parameter fully documented in the schema (including examples). The description lists the same categories in its text, adding no extra semantic meaning beyond what the schema provides. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate with additional parameter insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a random emoji from EmojiHub, optionally filtered by category.' It specifies the verb ('Get'), resource ('emoji'), and scope ('random' with optional category filtering). It distinguishes itself from sibling tools (e.g., culture__aic-artworks, culture__bible-api) by focusing on emoji retrieval, making the purpose highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it mentions the optional category filtering and lists all available categories (e.g., 'smileys-and-people', 'animals-and-nature'). However, it does not explicitly state when to use this tool versus alternatives (e.g., other culture tools like culture__colormind or culture__free-dictionary) or any exclusions, so it lacks explicit sibling differentiation beyond the purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (Free Dictionary API with CC BY-SA 3.0 license), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }' with quality scores and citation info). Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description complements these with practical implementation details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with two sentences that efficiently cover purpose, features, source, and output format without unnecessary details. Every sentence adds value, and it is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a read-only lookup with two parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adequately explains what the tool does, its source, and output structure, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not add meaning beyond the input schema, which has 100% coverage with clear descriptions for both parameters ('word' and 'language'). The baseline score of 3 is appropriate since the schema fully documents the parameters, and the description's mention of 'Supports multiple languages' only loosely relates to the 'language' parameter without adding specifics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('look up') and resources ('word definitions, phonetics, synonyms, and usage examples'), and distinguishes it from siblings by specifying it uses the Free Dictionary API for cultural/reference purposes, unlike other tools in the list that focus on different domains like agriculture, crime, or economics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Look up word definitions... from the Free Dictionary API') and mentions support for multiple languages, but does not explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., culture__wiktionary for similar reference purposes).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits, but the description specifies that it 'updates daily,' describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality scoring and citation details. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with two sentences that efficiently convey the tool's purpose, source, update frequency, and return format. Every sentence adds essential information without redundancy, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with metadata return), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers the tool's function, data source, update behavior, and output structure, providing sufficient context for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for 'query' and 'limit' parameters. The description does not add any additional semantic details about these parameters beyond what the schema provides, such as examples of query formats or usage tips. Thus, it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Search Project Gutenberg's catalog of over 70,000 free ebooks via the Gutendex API.' It specifies the verb ('search'), resource ('ebooks'), and scope ('Project Gutenberg's catalog'), and distinguishes itself from siblings by focusing on book metadata from a specific public domain source, unlike other culture tools like 'culture__open-library' or 'culture__bible-api'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for searching free ebooks in the public domain via the Gutendex API, with daily updates. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as 'entertainment__book-search' or 'culture__open-library', which might offer different book-related data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations by specifying the data source (Library of Congress), update frequency (daily), return format (Katzilla envelope), and details about quality scoring and citation components, though it doesn't mention rate limits or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, the second details the return format and components. Every element adds value without redundancy, making it appropriately concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and the presence of an output schema, the description is complete. It covers purpose, data source, update frequency, and return format, addressing key contextual needs without needing to explain parameters or return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (query and limit). The description does not add any parameter-specific semantics beyond what the schema provides, maintaining the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the Library of Congress digital collections for specific resource types (books, photographs, maps, manuscripts), distinguishes it from sibling tools by specifying the data source (Library of Congress), and includes the verb 'search' with the resource 'digital collections'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (searching Library of Congress collections) and mentions the data source and update frequency, but does not explicitly state when not to use it or name specific alternative tools among the many siblings listed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide: it discloses the data source ('Wikimedia REST API'), update frequency ('updates daily'), licensing ('CC BY-SA 3.0'), and detailed output structure ('Katzilla envelope { data, quality, citation }'). While annotations already indicate read-only, non-destructive, idempotent, and open-world characteristics, the description enriches this with practical implementation details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality and context, the second details the output format and metadata. Every element serves a purpose with no wasted words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, data source, licensing, update frequency, and output structure - addressing all aspects an agent needs to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (title and language). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't provide extra value regarding parameter usage or semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('retrieve a summary') and resources ('any Wikipedia article'), and distinguishes it from sibling tools by specifying it's for Wikipedia articles (unlike other culture tools like wiktionary or met-museum). The mention of 'Katzilla envelope' output format further differentiates it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('retrieve a summary of any Wikipedia article') and mentions support for multiple languages, but doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools (e.g., wiktionary for dictionary definitions).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it discloses the data source and licensing ('Source: The Metropolitan Museum of Art (CC0 1.0)'), update frequency ('updates daily'), and the specific return format ('Katzilla envelope { data, quality, citation }') with details about quality scoring and citation contents.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: purpose statement, source/licensing information, and return format details. Every sentence adds essential information with zero waste. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, source, licensing, update frequency, and detailed return format - addressing all aspects an agent needs beyond what's in structured fields. The output schema means the description doesn't need to explain return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (query and limit). The description doesn't add any parameter-specific semantics beyond what's in the schema descriptions, though it implies the query parameter's purpose through the collection context. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search'), resource ('The Metropolitan Museum of Art's collection of over 470,000 artworks'), and scope ('detailed object metadata including artist, period, medium, and images'). It distinguishes itself from sibling tools like 'culture__smithsonian' or 'culture__aic-artworks' by specifying the exact museum collection and metadata format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search The Metropolitan Museum of Art's collection') and implies usage for art-related queries. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, though the museum specificity helps differentiate it from other culture tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a read-only, non-destructive, idempotent, and open-world operation. The description adds valuable context beyond this: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 data hash'). This enriches the agent's understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose and source, the second details the return format and its components. Every phrase adds necessary information without redundancy, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the tool's purpose, usage context, return structure, and data quality aspects, leaving no significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the two parameters ('query' and 'limit'). The description adds minimal semantic value beyond the schema: it notes that omitting 'query' returns recent datasets, which is implied but not explicit in the schema's 'optional' note. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search openAFRICA'), resource ('dataset metadata from governments, NGOs, and researchers'), and scope ('largest independent open data portal on the African continent'). It distinguishes itself from sibling tools by focusing on African open data, unlike other culture tools like 'culture__met-museum' or 'culture__open-library'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: searching for African open data datasets. It implies usage by mentioning the source and update frequency ('updates daily'), but does not explicitly state when not to use it or name alternative tools for similar data from other regions, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world operations. The description supplements this by specifying the data source ('Open Library / Internet Archive'), update frequency ('updates daily'), licensing ('Public Domain (metadata)'), and the structure of the return envelope ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation components. This enriches the agent's understanding without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers the tool's purpose, scope, and output, and the second details the source, updates, and return format. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with metadata return), rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers the tool's purpose, behavioral traits, data source, update frequency, licensing, and output structure, providing sufficient context for an agent to use it effectively without needing to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for 'query' (search query types) and 'limit' (result count with constraints). The description does not add any additional parameter semantics beyond what the schema provides, such as search syntax examples or result ordering. Given the high schema coverage, a baseline score of 3 is appropriate, as the description relies on the schema for parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search Open Library's catalog'), the resource ('over 20 million books'), and the output ('Returns book metadata including title, author, publication year, and cover images'). It clearly distinguishes this as a search tool for book metadata from Open Library, differentiating it from other culture tools like 'culture__gutendex' (which focuses on Project Gutenberg books) or 'entertainment__book-search' (which appears to be a general book search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching Open Library's catalog of books. It implies usage by stating the source ('Open Library / Internet Archive') and the type of data returned (metadata). However, it does not explicitly mention when not to use it or name specific alternatives among the sibling tools (e.g., 'culture__gutendex' for public domain ebooks or 'entertainment__book-search' for broader book searches), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and behavior. The description adds valuable context beyond annotations: it specifies the source (PoetryDB, Public Domain), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation for audit). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with two sentences that efficiently convey purpose, scope, source, and return format. Every sentence adds value: the first defines the tool's function, and the second explains output details and source context, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with two parameters), rich annotations (covering read-only, non-destructive, idempotent, open-world), and the presence of an output schema (implied by description of return format), the description is complete. It covers purpose, usage context, behavioral traits, and output details, making it sufficient for an AI agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for both parameters (field with enum and value). The description adds minimal semantics beyond the schema, mentioning 'Search PoetryDB for classic poetry by title, author, or lines' which aligns with the schema but does not provide additional details like search syntax or examples. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search PoetryDB for classic poetry by title, author, or lines.' It specifies the verb ('Search'), resource ('PoetryDB'), and scope ('classic poetry'), and distinguishes itself from sibling tools by focusing on poetry search, unlike other culture tools like art, dictionary, or library searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching classic poetry via specific fields (title, author, lines). It mentions the source (PoetryDB) and updates daily, which implies freshness. However, it does not explicitly state when not to use it or name alternatives among siblings, such as using other culture tools for non-poetry searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable context beyond annotations: it discloses the source ('Al Quran Cloud'), update frequency ('updates daily'), and details about the return envelope structure ('Katzilla envelope { data, quality, citation }') with quality metrics and citation components.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and key features, the second provides source and output details. Every sentence adds essential information with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, source, update behavior, and output structure, leaving technical details to the structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description mentions 'surah:ayah notation' and 'various editions/translations', which aligns with but doesn't add significant meaning beyond the schema's parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with specific verbs ('retrieve Quran ayahs') and resources ('by reference with multiple translations'), and distinguishes it from siblings by specifying it's for Quran verses (unlike other culture tools like Bible API or poetry).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use it ('retrieve Quran ayahs by reference') and mentions the source ('Al Quran Cloud'), but doesn't explicitly state when not to use it or name specific alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description adds valuable context: it specifies the data source (Smithsonian Institution, CC0), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). This enhances transparency beyond the annotations without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source and return details in a structured manner. Every sentence adds essential information (e.g., data source, update frequency, return format), with no wasted words, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with structured returns), the description is complete: it covers purpose, source, update frequency, and detailed return format. With annotations providing safety hints and an output schema existing, no further explanation of behavior or return values is needed, making it fully adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the 'query' and 'limit' parameters. The description does not add any additional meaning or syntax details for these parameters beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), the resource ('Smithsonian Institution's Open Access collection of art, artifacts, and scientific specimens'), and distinguishes it from siblings by specifying the source and data type (e.g., different from 'culture__aic-artworks' or 'culture__met-museum'). It clearly defines the tool's function beyond a tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (searching Smithsonian collections) and implies alternatives by mentioning the specific source and data format. However, it does not explicitly name when not to use it or list specific sibling alternatives, keeping it at a high but not perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it discloses the data source (Wiktionary/Wikimedia Foundation), licensing (CC BY-SA 3.0), update frequency (daily), and detailed return structure (Katzilla envelope with quality scores and citation details including SHA-256 hash). This significantly enhances behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first establishes purpose and scope, the second details return format and metadata. Every element serves a clear purpose with zero wasted words, and critical information (tool purpose) is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter lookup), rich annotations covering safety and behavior, and existence of an output schema, the description provides excellent contextual completeness. It adds source attribution, licensing, update frequency, and detailed return format explanation that complements the structured fields perfectly for this reference tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a single parameter 'word' clearly documented. The description doesn't add any parameter-specific semantics beyond what the schema provides (e.g., no examples, format requirements, or language constraints). With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific verb ('look up') and resource ('word definitions, etymology, and usage from Wiktionary'), and distinguishes it from siblings by specifying it's a multilingual dictionary with over 7 million entries. It explicitly mentions the source (Wiktionary/Wikimedia Foundation) and license, which differentiates it from other reference tools like 'culture__free-dictionary' or 'culture__bible-api'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Look up word definitions, etymology, and usage from Wiktionary'), but doesn't explicitly state when not to use it or name specific alternatives among siblings. The mention of 'multilingual dictionary' and source details implies it's for comprehensive word research, but lacks explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format (JSON-stat), update frequency (monthly), and the structure of the response envelope ({ data, quality, citation }) with details on quality scores and citation contents. This enhances understanding of the tool's behavior without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, and the second details the return format, update frequency, and response envelope. Every sentence adds essential information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (querying a statistical database with multiple parameters), the description is complete. It covers purpose, usage context, behavioral traits (like update frequency and response structure), and there is an output schema (implied by 'Has output schema: true'), so return values need not be explained. The annotations and schema provide additional structured information, making the description well-rounded.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for 'dataset', 'geo', and 'time' parameters. The description does not add specific semantics beyond what the schema provides, such as examples or constraints not in the schema. Given the high schema coverage, the baseline score of 3 is appropriate, as the description relies on the schema for parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query Eurostat for European statistical data including GDP, population, employment, trade, and more.' It specifies the verb ('query'), resource ('Eurostat'), and scope ('European statistical data'), and distinguishes it from sibling tools like 'demographics__census-acs' or 'economic__eurostat-gdp' by covering a broader range of data types beyond just demographics or specific economic indicators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for querying Eurostat data, returns JSON-stat format, updates monthly, and includes quality and citation information. However, it does not explicitly state when to use this tool versus alternatives like 'economic__eurostat-gdp' or 'trade__eurostat-trade', which are more specific sibling tools. The guidance is implied but not explicit about exclusions or direct comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the source (Nager.Date API with MIT License), update frequency ('updates annual'), and return format details ('Katzilla envelope { data, quality, citation }' with quality metrics and citation info). Annotations already indicate read-only, non-destructive, idempotent, and open-world traits, so the description complements these with practical implementation details without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details in a logical flow. Each sentence adds value: coverage info, source attribution, and return format explanation. There is no redundant or verbose content, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits, source info, and return format, leaving no significant gaps for an agent to understand and invoke the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters ('year' and 'countryCode') well-documented in the input schema. The description mentions the parameters implicitly ('for any country and year') but does not add significant semantic details beyond what the schema provides, such as format examples or constraints not already covered. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get public holidays') and resources ('for any country and year using the Nager.Date API'). It distinguishes itself from sibling tools by specifying its unique domain (public holidays) and data source (Nager.Date API), unlike other demographics tools that focus on census data, economic indicators, or country information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Get public holidays for any country and year') and mentions the coverage ('Covers 100+ countries with holiday types and regional info'). However, it does not explicitly state when not to use it or name specific alternative tools for similar data, though the sibling list shows no direct holiday alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the return format ('Returns the Katzilla envelope { data, quality, citation }'), explains quality scoring ('quality scores freshness/uptime/confidence'), and details citation components ('citation carries the source URL, license, and a SHA-256 data hash for audit'). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, search methods, source, and updates; the second explains the return format and its components. Every sentence adds critical information (e.g., data scope, search options, return envelope details) with zero waste. It is front-loaded with the core functionality and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering read-only, non-destructive, idempotent, open-world), 100% schema description coverage, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It adds necessary context like the return envelope structure, quality scoring, and citation details, which the output schema likely documents but are usefully summarized. No gaps are evident for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('endpoint' and 'value') well-documented in the input schema. The description adds minimal parameter semantics beyond the schema: it mentions 'Search by name, alpha code, or region,' which aligns with the enum values in the schema but does not provide additional syntax, format details, or examples. Given high schema coverage, the baseline score of 3 is appropriate as the description does not significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Look up country information including population, capital, region, languages, currencies, and flags.' It specifies the verb ('look up'), resource ('country information'), and detailed scope of data returned. It distinguishes from siblings by focusing on demographics/population data from REST Countries, unlike other tools in the list (e.g., agriculture, crime, economic data tools).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Search by name, alpha code, or region' and mentions the source and update frequency ('Source: REST Countries, updates annual'). However, it does not explicitly state when to use this tool versus alternatives (e.g., other demographics tools like demographics__census-acs or demographics__eurostat), nor does it provide exclusions or prerequisites. The guidance is helpful but lacks sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable context beyond annotations: it specifies the source (Bank of Canada), update frequency (daily), return format (Katzilla envelope with data, quality, citation), and details on quality scoring and citation contents (URL, license, SHA-256 hash). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source, update frequency, and return format details. Every sentence adds value: the first defines the tool, the second specifies source and updates, and the third explains the return structure. No wasted words, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (financial data retrieval), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, source, update behavior, and output structure, compensating for any gaps. No additional explanation is needed for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters (series, startDate, endDate). The description does not add parameter-specific semantics beyond what the schema provides, such as explaining series naming conventions or date format implications. It mentions common series examples, but these are already listed in the schema's description for the series parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: retrieving 'interest rates and exchange rates from the Bank of Canada,' with specific examples (policy interest rate, prime rate, government bond yields, CAD exchange rates). It clearly distinguishes from siblings by focusing on Bank of Canada data, unlike other economic tools (e.g., economic__ecb-rates for European Central Bank or economic__exchange-rates for general exchange rates).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use it: for Bank of Canada interest and exchange rate data, updated daily. It implies alternatives by specifying the source, but does not explicitly name when not to use it or list specific sibling alternatives (e.g., economic__ecb-rates for European data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context: the data source (ECB), update frequency (daily), return format (Katzilla envelope with quality scores and citation details), and audit features (SHA-256 hash), which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source, update frequency, and return format details in a single, efficient sentence. Every element adds value without redundancy, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no nested objects), high schema coverage (100%), comprehensive annotations, and presence of an output schema, the description is complete. It effectively supplements structured data with source, update frequency, and return format details, leaving no significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (currency and lastN) well-documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as examples or constraints, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get daily exchange rates'), resource ('from the European Central Bank'), and scope ('daily'), distinguishing it from sibling tools like economic__exchange-rates by specifying the source (ECB) and data type (exchange rates).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context for ECB-specific exchange rate data, but does not explicitly state when to use this tool versus alternatives like economic__exchange-rates or economic__boc-rates. It provides source information but lacks explicit comparisons or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it discloses the update frequency ('updates monthly'), describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality metrics and citation details. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, and data details; the second explains the return format and its components. Every sentence adds critical information (e.g., source, update frequency, output structure) with zero waste, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (economic data retrieval), the description is complete: it covers purpose, data source, coverage, update frequency, and output format. With annotations handling safety/behavioral traits and an output schema presumably detailing the 'Katzilla envelope', the description provides all necessary contextual information without redundancy, ensuring the agent can use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage with detailed enum explanations (e.g., geo codes, unit meanings, indicator mappings). The description does not add further parameter semantics beyond what the schema provides, such as examples of valid geo codes or clarification on unit differences. With high schema coverage, a baseline score of 3 is appropriate as the description relies on the schema for param details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool retrieves 'GDP and main aggregates from Eurostat national accounts', specifying the data source (Eurostat), coverage (EU/EEA member states), frequency (quarterly/annual), and key metrics (GDP, household consumption, etc.). It clearly distinguishes from siblings like economic__eurostat-inflation or economic__eurostat-unemployment by focusing on GDP and related aggregates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for Eurostat GDP data across EU/EEA states. It implies usage for economic analysis but does not explicitly state when not to use it or name specific alternatives among siblings (e.g., economic__bea-gdp for US data). The mention of 'Source: Eurostat' and coverage details offers good guidance without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source (Eurostat), update frequency (monthly), return format (Katzilla envelope with data, quality, citation), and details about quality scores and citation contents (URL, license, SHA-256 hash). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and context; the second details the return format and its components. Every element adds value without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), 100% schema coverage, and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, usage context, behavioral details, and output structure, leaving no significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the 'geo' and 'coicop' parameters with defaults and enums. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining the significance of geo codes or COICOP categories in context. Thus, it meets the baseline but does not enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves Harmonised Index of Consumer Prices (HICP) inflation data from Eurostat, specifying it's monthly data for EU/EEA countries and the standard measure for ECB policy. It distinguishes itself from siblings like economic__eurostat-gdp or economic__eurostat-unemployment by focusing on inflation metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage (ECB monetary policy decisions, monthly updates) and implies when to use it (for inflation data vs. other economic indicators). However, it does not explicitly state when not to use it or name specific alternative tools for similar data, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the return format (Katzilla envelope with data, quality, citation), explains what quality scores measure (freshness/uptime/confidence), and describes citation contents (source URL, license, SHA-256 hash). While annotations cover read-only/non-destructive/idempotent/open-world aspects, the description provides important implementation details about output structure and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, and parameters; the second explains the return format and its components. Every element serves a clear purpose with no wasted words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and the presence of an output schema (implied by the detailed return format description), the description provides complete context. It covers purpose, usage context, behavioral traits, and output structure without needing to duplicate structured field information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage and clear enum values for 3 of 4 parameters, the schema already documents parameter semantics thoroughly. The description mentions 'broken down by age and sex' and 'seasonally adjusted' which aligns with parameters but doesn't add significant meaning beyond what the schema provides. The baseline of 3 is appropriate when the schema does most of the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves 'Monthly and annual unemployment rates from Eurostat for EU/EEA countries' with specific breakdowns by 'age and sex' and seasonal adjustment status. It distinguishes itself from sibling tools like economic__eurostat-gdp or economic__eurostat-inflation by focusing specifically on unemployment data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool: for European labour market analysis with Eurostat unemployment data. It mentions the data source and update frequency, but doesn't explicitly state when not to use it or name specific alternatives among the many economic sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations: it discloses the data source (Frankfurter API), licensing (MIT License), update frequency (real-time), and return format (Katzilla envelope with quality scores and citation details). The annotations already indicate it's read-only, non-destructive, idempotent, and open-world, but the description enriches this with practical implementation details like no API key requirement and audit features, without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and key features; the second details the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, source, licensing, update behavior, and return format, compensating well for any gaps and ensuring the agent has sufficient context without needing to rely solely on structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the baseline is 3. The description does not add specific parameter semantics beyond what the schema provides (e.g., it doesn't explain format details for 'date' or examples for 'symbols' beyond the schema's descriptions). However, it implicitly contextualizes parameters by mentioning 'current or historical' data, which aligns with the 'date' parameter, but this is minimal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get current or historical currency exchange rates'), resource ('from the Frankfurter API'), and scope ('Supports all major currencies'). It distinguishes itself from sibling tools by focusing exclusively on exchange rates, unlike other economic tools like 'economic__bea-gdp' or 'economic__bls-series' which handle different economic indicators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it specifies the data source (Frankfurter API), indicates it's for currency exchange rates, and mentions it supports both current and historical data. However, it does not explicitly state when to use this tool versus alternatives (e.g., other exchange rate tools like 'economic__ecb-rates' or 'international__open-exchange'), nor does it provide exclusions or prerequisites beyond the implied currency code usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description adds valuable behavioral context: it discloses the data source ('Federal Reserve Economic Data'), update frequency ('updates daily'), and detailed return structure ('Katzilla envelope with quality scores and citation'), which are not captured in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by essential details like source, update frequency, and return structure in two efficient sentences with zero wasted words, making it highly readable and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete: it covers purpose, source, behavior, and output details without needing to explain return values, which are handled by the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the 'query' and 'limit' parameters. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('Search for'), resource ('economic data series in the FRED database'), and scope ('by keyword'), and distinguishes it from siblings by specifying the FRED database context, which is unique among economic tools like 'economic__bea-gdp' or 'economic__bls-series'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search for economic data series in the FRED database by keyword'), but does not explicitly state when not to use it or name alternatives among siblings, such as 'economic__fred-series' for retrieving specific series details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source ('Federal Reserve Economic Data (Public Domain)'), update frequency ('updates daily'), and return format details ('Katzilla envelope { data, quality, citation }' with quality scores and citation info). No contradictions with annotations are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details in a structured manner. Every sentence adds value: the first defines the action, the second lists examples and scope, the third specifies source and updates, and the fourth explains the return format. There is no wasted text, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (data fetching with multiple parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adequately explains the tool's purpose, behavior, and output without needing to detail return values, as the output schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all four parameters (seriesId, limit, startDate, endDate) with examples and constraints. The description does not add any additional meaning or clarification about the parameters beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Fetch'), resource ('time-series observations from the Federal Reserve Economic Data (FRED) API'), and scope ('GDP, unemployment, CPI, and thousands of other economic indicators'). It distinguishes itself from sibling tools like 'economic__fred-search' by focusing on fetching observations rather than searching for series.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Fetch time-series observations'), but it does not explicitly state when not to use it or name alternatives. It implies usage for economic data retrieval but lacks explicit exclusions or comparisons to other data-fetching tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it discloses the update frequency (monthly), source attribution (IMF Terms of Use), and details about the return format (Katzilla envelope with quality scores and citation). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, the second covers behavioral details and return format. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, rich annotations (read-only, idempotent, open world), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits (updates, source, return format), and distinguishes from siblings, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting all four parameters with examples and defaults. The description adds minimal parameter semantics, only implying the scope (multi-year ranges, 190+ countries) without providing additional syntax or constraints beyond the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get economic indicators from the IMF World Economic Outlook (WEO) DataMapper.' It specifies the data source (IMF), scope (190+ countries, multi-year ranges back to 1980), and examples of indicators (GDP growth, inflation, etc.), distinguishing it from sibling tools like economic__bea-gdp or economic__world-bank by focusing on IMF-specific data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for IMF economic indicators with support for multi-year ranges and forecasts. It implicitly suggests alternatives by mentioning the IMF source, but does not explicitly name when not to use it or compare with specific sibling tools like economic__fred-series or economic__oecd-indicators.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the source (OECD), update frequency (monthly), return format (Katzilla envelope with data, quality, citation), and audit features (SHA-256 hash). While annotations cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and updates; the second details the return format and its components. Every element serves a clear purpose with no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema, the description is complete. It covers source, update frequency, return structure, and auditability, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents both parameters (country ISO code and lastN count). The description does not add any parameter-specific semantics beyond what the schema provides, so it meets the baseline expectation without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get key economic indicators') and resources ('OECD SDMX API'), distinguishing it from sibling tools like 'economic__bea-gdp' or 'economic__world-bank' by specifying the OECD source and monthly updates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (for OECD economic indicators with monthly updates), but does not explicitly state when not to use it or name specific alternatives among the many sibling economic tools, leaving some ambiguity about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source ('U.S. Department of the Treasury'), update frequency ('updates daily'), return format ('Katzilla envelope'), and quality metrics ('freshness/uptime/confidence'). This provides practical implementation details the annotations don't cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that each serve distinct purposes: the first covers purpose and scope, the second covers source, update frequency, and return format. There is no wasted verbiage, and critical information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations, complete schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, data source, temporal scope, update frequency, and return format - everything needed to understand the tool's behavior without duplicating structured field information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all four parameters. The description mentions 'date range filtering' which aligns with startDate/endDate parameters, but adds no additional semantic meaning beyond what's in the schema descriptions. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Fetch US national debt data'), resource ('from the Treasury Fiscal Data API'), and scope ('total public debt outstanding and debt held by the public'). It distinguishes itself from sibling tools like 'economic__treasury-fiscal-data' by focusing specifically on debt data rather than broader fiscal data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Supports date range filtering for historical data back to 1993'), but does not explicitly state when to use this tool versus alternatives like 'economic__treasury-fiscal-data' or other economic data tools. The historical data range guidance is helpful but not comprehensive about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source (U.S. Department of the Treasury), update frequency (daily), return format (Katzilla envelope with data, quality, citation), and audit features (SHA-256 hash). No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, and usage; the second details source, updates, and return format. Every sentence adds critical information without redundancy, making it front-loaded and zero-waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, 100% schema coverage, annotations, and an output schema implied by the return format description), the description is complete. It covers purpose, usage, behavioral traits, source details, and output structure, compensating well for any gaps. With annotations and schema handling technical aspects, no additional explanation is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters (dataset, startDate, endDate, limit, sort). The description adds minimal parameter semantics beyond the schema, only mentioning dataset selection and date-range filtering generically, which aligns with the schema but doesn't provide extra details like dataset examples or format specifics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Access'), resource ('100+ Treasury fiscal datasets'), and scope ('interest rates, revenue, spending, securities, and more'), making the purpose highly specific. It distinguishes itself from sibling tools like 'economic__treasury-debt' by focusing on broader fiscal data rather than just debt, and from other economic tools by specifying the Treasury source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for accessing Treasury fiscal datasets with date-range filtering. It implies usage for economic/financial data needs but does not explicitly state when not to use it or name specific alternatives among siblings (e.g., 'economic__bls-series' for labor data), though the dataset list helps differentiate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits, but the description discloses the return format ('Katzilla envelope { data, quality, citation }'), data quality metrics ('freshness/uptime/confidence'), and audit features ('SHA-256 data hash'). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality and data returned, and the second details the output format, quality metrics, and source information. Every sentence adds essential value without redundancy, making it front-loaded and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search API with structured output), the description is complete. It covers purpose, data source, update frequency, and detailed output behavior. With annotations covering safety traits and an output schema presumably detailing the 'Katzilla envelope', no critical gaps remain for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents all three parameters (query, limit, fields). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining field selection strategies or query formatting nuances. Thus, it meets the baseline but doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Search U.S. colleges and universities via the Department of Education College Scorecard API' with specific resources mentioned ('admissions rates, tuition costs, and enrollment size'). It clearly distinguishes from sibling tools by specifying the exact data source (College Scorecard API) and domain (education), unlike other education tools like 'education__ed-demographics' or 'education__hipolabs-universities'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching U.S. colleges via the College Scorecard API with specific data types. It mentions the source and update frequency ('updates quarterly'), which helps guide usage. However, it does not explicitly state when not to use it or name specific alternatives among siblings, preventing a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (Hipolabs University Domains), update frequency (annual), and details about the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). This enriches the agent's understanding of data reliability and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, filtering, and data source, while the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no nested objects), rich annotations, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage context, data source, update frequency, and return format details, providing sufficient context for an agent to understand and invoke the tool effectively without needing to explain return values redundantly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('country' and 'name') well-documented in the schema. The description adds minimal semantic value beyond the schema, mentioning filtering by country and name but not providing additional syntax or format details. Given the high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('search universities worldwide') and resources ('using the Hipolabs University Domains API'), and it distinguishes from siblings by specifying its unique domain (education data) and data source (Hipolabs). Unlike generic search tools in the sibling list, this is explicitly for university data with annual updates from an open data source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('search universities worldwide' with filtering by country and name), but it does not explicitly state when not to use it or name specific alternatives. While it implies usage for finding institution details, domains, and web pages, it lacks explicit exclusions or comparisons to other education tools like 'education__college_scorecard' in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (Department for Education), licensing (Open Government Licence), update frequency (annual), and return format (Katzilla envelope with quality scores and citation details). While annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, the description provides important operational details about data provenance and structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: first establishes purpose and scope, second specifies source and licensing, third details return format. Every sentence adds essential information with zero wasted words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), complete schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers data source, licensing, update frequency, and return format details that aren't captured in structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters. The description doesn't add specific parameter semantics beyond what's in the schema, though it implies the 'query' parameter searches education datasets. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving UK education statistics from the Department for Education, specifying the data domains (school performance, pupil characteristics, workforce data, educational outcomes) and geographic scope (England). It distinguishes itself from sibling tools like 'education__college_scorecard' by focusing on UK government data rather than US college data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool: for UK education statistics with annual updates from the Department for Education. It doesn't explicitly state when not to use it or name specific alternatives, but the geographic and topical focus gives strong implicit guidance compared to other education tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (Eurostat with copyright/license policy), update frequency (monthly), and detailed return structure (Katzilla envelope with quality scores and citation details including SHA-256 hash). While annotations already indicate read-only, non-destructive, idempotent, and open-world characteristics, the description enriches this with practical implementation details that help the agent understand data provenance and quality assessment.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first establishes purpose and scope, the second covers source, updates, and return format. Every element serves a clear purpose with zero redundant information. It's appropriately front-loaded with the core functionality before detailing implementation specifics.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters with enums), comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers data source, update frequency, return structure with quality metrics, and licensing information - all valuable additions beyond what structured fields provide.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage and comprehensive enum documentation for geo, nrg_bal, and siec parameters, the schema already provides complete parameter information. The description doesn't add any parameter-specific semantics beyond what's in the schema, but it does provide overall context about the data domain that helps interpret parameter choices. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: retrieving 'Energy supply, transformation, and consumption data from Eurostat' with specific coverage details (EU/EEA countries, annual data on primary energy production, imports, final consumption, energy dependency). It clearly distinguishes itself from sibling tools by specifying its domain (energy) and data source (Eurostat), unlike siblings like agriculture__usda-nass or economic__eurostat-gdp.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: for Eurostat energy data covering EU/EEA countries with monthly updates. It mentions the return format (Katzilla envelope) which helps understand output structure. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings (e.g., economic__eurostat-gdp for economic data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source ('NREL Census Rate API'), update frequency ('updates monthly'), and details about the return structure ('Katzilla envelope { data, quality, citation }') including quality metrics and citation information. This enhances understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second explains the return format and additional details. Every sentence adds value without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return structure, compensating for any gaps. The output schema likely details the 'Katzilla envelope', so the description need not explain return values exhaustively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for 'lat' and 'lon' parameters. The description does not add any additional parameter semantics beyond what the schema provides, such as format examples or constraints. However, with full schema coverage, the baseline score of 3 is appropriate as the schema adequately documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('look up utility rates by census region') and identifies the resource ('from the National Renewable Energy Laboratory'). It distinguishes itself from sibling tools by focusing on utility rates and census regions, unlike other energy tools like 'energy__eurostat-energy' or 'energy__nrel-building-components'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('look up utility rates by census region for a given location'), but does not explicitly mention when not to use it or name alternative tools. It implies usage for location-based utility rate queries without specifying exclusions or comparisons to other data sources.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source (Open Library API with CC0 1.0 license), update frequency ('updates daily'), and output format details (Katzilla envelope with quality scores and citation info). Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description enriches this with operational specifics like freshness and auditability, without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential behavioral details in subsequent sentences. Every sentence adds critical information (e.g., data source, output format), with zero waste or redundancy, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits (source, updates, output format), and aligns with structured data, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions search by 'title, author, or keyword,' which aligns with the 'query' parameter in the schema, but adds no additional semantic details beyond what the schema provides (e.g., no examples or advanced usage). With 100% schema description coverage, the baseline is 3, as the schema fully documents parameters, and the description offers minimal extra value here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Search for books by title, author, or keyword using the Open Library API.' It specifies the verb ('Search'), resource ('books'), and scope ('by title, author, or keyword'), and distinguishes it from sibling tools by mentioning the specific API (Open Library), which none of the other tools use for book searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: for searching books via the Open Library API. It does not explicitly state when not to use it or name alternatives, but the specificity of the API and data source (Open Library) implies it's the primary tool for this purpose among siblings, which include general search tools like 'culture__open-library' but not identical book-search functions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only, non-destructive, idempotent, and open-world behavior, which the description does not contradict. The description adds valuable context beyond annotations: it specifies the data source (Environment and Climate Change Canada), update frequency ('updates daily'), and return format details (Katzilla envelope with quality scores and citation info), enhancing transparency about data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by scope, source, and return format details in a logical flow. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, scope, source, update frequency, and return structure, compensating well for any gaps. With annotations and output schema handling safety and return values, no critical information is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear parameter descriptions in the schema. The description does not add any additional meaning or examples for the parameters beyond what the schema provides, such as explaining typical use cases for 'province' or 'limit'. However, it meets the baseline of 3 since the schema adequately documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: retrieving 'active weather alerts and warnings from Environment and Climate Change Canada via the MSC GeoMet API.' It specifies the scope ('Covers all provinces and territories') and the types of alerts ('severe weather warnings, watches, advisories, and special statements'), clearly distinguishing it from other environment tools like climate data or air quality tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for real-time severe weather alerts in Canada, with daily updates. It implies usage for safety or monitoring purposes but does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as 'hazards__nws-alerts' for U.S. alerts or other environment tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations by specifying the data source license ('CC-BY 4.0'), update frequency ('updates daily'), and details about the return structure ('quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit'), enhancing transparency without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format and quality metrics. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It explains the data source, update frequency, return structure, and quality aspects, compensating adequately without needing to detail parameters or output values further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for all parameters (country, since, to). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining the implications of the 'since' and 'to' defaults or the data lag mentioned in the schema, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get'), resource ('country-level greenhouse gas emissions data'), and source ('Climate TRACE'), distinguishing it from siblings like environment__climate-trace-assets or environment__climate-trace-sectors by focusing on country-level data rather than assets or sectors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying the data source, update frequency ('updates daily'), and return format ('Katzilla envelope'), but does not explicitly state when to use this tool versus alternatives like environment__epa-ghg or environment__carbon-intensity, nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source ('Climate TRACE (CC-BY 4.0)'), update frequency ('updates daily'), return structure ('Katzilla envelope'), and details about quality scores ('freshness/uptime/confidence') and citation ('source URL, license, SHA-256 data hash'). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and source, and the second details the return format and its components. Every sentence adds essential information (e.g., data source, update frequency, return structure, quality metrics, citation details) with zero wasted words, making it highly concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, read-only operation), the description is complete. It covers purpose, source, update frequency, return format, and behavioral context. With annotations handling safety profiles and an output schema presumably detailing the 'Katzilla envelope', the description provides all necessary context without redundancy, making it fully adequate for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all four parameters (country, sector, year, limit) with defaults, constraints, and examples. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining sector nuances or year lag implications. Given the high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Get asset-level (facility/power plant) greenhouse gas emissions data from Climate TRACE.' It specifies the verb ('Get'), resource ('asset-level greenhouse gas emissions data'), and source ('Climate TRACE'), distinguishing it from sibling tools like 'environment__climate-trace' (likely aggregated data) and 'environment__climate-trace-sectors' (sector-level data).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning the data source, update frequency ('updates daily'), and return format ('Katzilla envelope'). However, it does not explicitly state when to use this tool versus alternatives (e.g., 'environment__climate-trace' or 'environment__climate-trace-sectors'), nor does it mention any prerequisites or exclusions, leaving some guidance gaps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source ('Climate TRACE (CC-BY 4.0)'), update frequency ('updates daily'), and return structure ('Katzilla envelope { data, quality, citation }'), including details on quality scores and citation components, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source, update frequency, and return details in a compact two-sentence format. Every sentence adds essential information without redundancy, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update behavior, and return structure, compensating well for any gaps and aligning with the structured data provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description does not add any parameter-specific details beyond what the schema provides, such as explaining the 'sector' enum values or 'since'/'to' defaults, but it implies the tool's purpose aligns with these parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Get sector-level greenhouse gas emissions data'), resource ('from Climate TRACE'), and scope ('broken down by industry sectors'), clearly distinguishing it from sibling tools like 'environment__climate-trace' or 'environment__climate-trace-assets' by focusing on sector-level data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('updates daily') and implies when to use it (for sector-level emissions data), but does not explicitly state when not to use it or name specific alternatives among siblings, such as 'environment__climate-trace' for broader data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the data source (EPA ECHO), update frequency ('updates daily'), and details the return format ('Katzilla envelope { data, quality, citation }') with quality metrics and citation info, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by source and update info, and concludes with return format details. Every sentence adds value: the first defines the action, the second provides metadata, and the third explains the output structure. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with filtering), rich annotations (covering safety and idempotency), 100% schema coverage, and an output schema (implied by return format description), the description is complete. It covers purpose, source, update frequency, and output structure, leaving no gaps for the agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for state (U.S. state/territory code), city (city name filter), and limit (number of results). The description adds no additional parameter semantics beyond what the schema provides, such as explaining how city filtering works or interaction between parameters. Baseline 3 is appropriate given the comprehensive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search'), resource ('EPA ECHO for environmental compliance and enforcement facilities'), and scope ('by state and city'). It distinguishes itself from siblings by specifying the data source (EPA ECHO) and domain (environmental compliance), unlike other environment tools like 'epa-aqs' or 'noaa-cdo'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: searching for environmental compliance facilities by state and city. It does not explicitly state when not to use it or name alternatives, but the specificity of the search parameters (state/city) implies it's for location-based queries, distinguishing it from broader environmental data tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (EPA GHGRP, Public Domain), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every sentence adds critical information (e.g., update frequency, return structure) with zero waste, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (querying EPA data), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context like data source, update frequency, and return format details, compensating adequately without needing to explain parameters or output values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('limit' and 'state') fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't clarify state abbreviation formats or limit constraints), so it meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query EPA's Greenhouse Gas Reporting Program data for facility-level direct emissions.' It specifies the verb ('query'), resource ('EPA GHGRP data'), and scope ('facility-level direct emissions'), distinguishing it from sibling tools like environment__epa-aqs (air quality) or environment__climate-trace (global emissions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for querying EPA GHGRP data, updated daily, with a specific return format. However, it does not explicitly state when to use this tool versus alternatives (e.g., environment__epa-aqs for air quality or environment__climate-trace for broader emissions), nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond this: it specifies the data source (NOAA National Centers for Environmental Information), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality scores and citation components. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with core purpose, efficiently covers key details (scope, source, updates, return format), and uses every sentence purposefully without waste. It balances completeness with brevity, making it easy for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, annotations, and an output schema), the description is complete. It covers purpose, scope, source, update frequency, and return structure. With annotations providing safety hints and an output schema presumably detailing the 'Katzilla envelope,' no critical gaps remain for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain dataType codes like 'TMAX' or locationId formats). Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving historical weather and climate observations (specific verb) from NOAA CDO (resource) for US locations. It distinguishes from siblings by specifying 'daily temperature, precipitation, snowfall, and wind' and being the 'largest archive of US weather data,' setting it apart from other environment tools like environment__canada-weather or environment__openaq.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Historical weather and climate observations... for any US location' and 'updates daily.' It implies when to use (for US historical weather data) but does not explicitly state when not to use or name specific alternatives among siblings, such as environment__canada-weather for non-US data or environment__openmeteo-aq for air quality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide. While annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, the description adds: data source attribution ('Source: OpenAQ (CC-BY 4.0)'), update frequency ('updates daily'), and detailed return format information ('Returns the Katzilla envelope { data, quality, citation } — quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit'). This provides important operational context about data provenance and quality metrics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that each earn their place. The first sentence states the core functionality and filtering options, while the second provides crucial metadata about source, update frequency, and return format. There's zero wasted text, and important information is front-loaded appropriately for agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, 100% schema coverage, and presence of an output schema, the description provides excellent contextual completeness. It covers purpose, data source, update frequency, and detailed return format information. With annotations handling safety/behavioral aspects and the output schema presumably documenting the return structure, the description focuses appropriately on the value-added context that structured fields don't capture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all 4 parameters (lat, lng, limit, radius) with their types, constraints, and descriptions. The description mentions 'optionally filtered by coordinates and radius' which aligns with the schema but doesn't add significant semantic value beyond what's already in the structured fields. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search for air quality monitoring locations'), resource ('from the OpenAQ platform'), and scope ('optionally filtered by coordinates and radius'). It distinguishes itself from sibling tools by focusing specifically on air quality monitoring locations rather than other environmental data sources like weather, carbon intensity, or water quality tools in the environment category.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Search for air quality monitoring locations... optionally filtered by coordinates and radius'), but doesn't explicitly mention when NOT to use it or name specific alternatives. It implies usage for location-based air quality data searches, but lacks explicit exclusion guidance for other types of environmental queries that might be handled by sibling tools like environment__waqi or environment__openmeteo-aq.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source (Open-Meteo with CC-BY 4.0 license), update frequency (real-time), and details about the return structure (Katzilla envelope with quality scores and citation including SHA-256 hash). This enhances understanding of data provenance and reliability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second explains the return format and additional metadata. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, behavioral traits, and output structure, leaving no significant gaps for an agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting latitude and longitude parameters with their constraints. The description does not add any parameter-specific information beyond what the schema provides, which is acceptable given the high schema coverage. The baseline score of 3 reflects adequate parameter documentation through the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get current air quality data'), resources (PM10, PM2.5, CO, NO2, O3), and source (Open-Meteo). It distinguishes from sibling tools by specifying air quality data rather than weather, climate, or other environmental data sources listed among siblings like environment__canada-weather or environment__openaq.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to obtain real-time air quality data for a given location from Open-Meteo. It does not explicitly mention when not to use it or name specific alternatives, but the context is sufficiently clear given the specialized nature of air quality data among environmental tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source ('openSenseMap'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation components. This significantly enhances understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and optional filtering, the second details the return format and source information. Every sentence adds value with no wasted words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations (readOnly, non-destructive, idempotent, openWorld), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, source, update frequency, return format structure, and data quality aspects - everything needed for effective tool selection and use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema. The description mentions 'optionally filtered by location' which aligns with the lat/lng/radius parameters, but doesn't add meaningful semantic details beyond what the schema already provides. The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search for environmental sensor stations'), resource ('senseBoxes from the openSenseMap platform'), and scope ('optionally filtered by location'). It distinguishes itself from sibling tools like 'environment__openaq' or 'environment__waqi' by specifying the exact platform and sensor type (senseBoxes).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search for environmental sensor stations... optionally filtered by location'), but does not explicitly state when not to use it or name specific alternatives among the many sibling tools. The optional filtering is mentioned, which helps guide usage decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source (Environment Agency), update frequency (daily), license (Open Government Licence), and the return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enriches the agent's understanding of data freshness, auditability, and reliability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the tool's purpose, scope, and source; the second explains the return format and its components. Every sentence adds essential information without redundancy, making it easy for an agent to parse and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no required ones), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by the description of the Katzilla envelope), the description is complete. It covers purpose, source, update frequency, license, and return structure, leaving no significant gaps for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all four parameters (type, county, stationId, limit) with descriptions and constraints. The description does not add any parameter-specific details beyond what the schema provides, such as examples of county names or stationId formats, but it doesn't need to given the comprehensive schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: providing real-time flood warnings and river/sea level monitoring from the UK Environment Agency. It specifies the data types (flood alerts, warnings, water level readings) and source, distinguishing it from sibling environment tools like environment__canada-weather or environment__noaa-cdo which cover different regions or data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (real-time flood monitoring in the UK) and mentions the data source and update frequency (daily). However, it does not explicitly state when not to use it or name specific alternatives among the many sibling tools, though the UK focus and flood data specialization naturally differentiate it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it specifies the data source (World Air Quality Index Project), update frequency (real-time), license (CC-BY-NC 4.0), and return format details (Katzilla envelope with quality scores and citation). This enhances the agent's understanding of data reliability and attribution.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and source, and the second details the return format and metadata. Every sentence adds critical information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations (covering safety and idempotency), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, license, and return structure, leaving no significant gaps for the agent to understand the tool's behavior and context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'city' parameter documented as 'City name or station identifier'. The description does not add any further parameter semantics beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get'), resource ('real-time Air Quality Index (AQI) data'), and scope ('for a city from the WAQI project'). It distinguishes itself from sibling tools by focusing on air quality data, unlike other environment tools that cover weather, carbon intensity, or floods.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (to retrieve real-time AQI data for a city), but it does not explicitly state when not to use it or name alternatives. It implies usage for air quality queries, but lacks explicit exclusions or comparisons with similar tools like environment__openaq or environment__openmeteo-aq.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations by specifying the source (FreeToGame), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details), which aids in understanding data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and key features, followed by source and return details. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, usage context, source information, update frequency, and return format, compensating adequately where structured data might not fully convey behavioral aspects like data freshness and citation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters (platform, category, sortBy) well-documented in the schema. The description adds minimal semantic value by mentioning filtering by platform and genre and sorting options, but does not provide additional details beyond what the schema already specifies, such as examples or usage nuances.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('browse free-to-play games') and resources ('games'), and distinguishes it from siblings by specifying its unique domain (free-to-play games) and source (FreeToGame). It goes beyond a tautology by detailing the action and scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by listing filter options (platform, genre) and sort criteria, but does not explicitly state when to use this tool versus alternatives or any exclusions. It implies usage through the filtering capabilities but lacks explicit guidance on alternatives or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the return format ('Katzilla envelope { data, quality, citation }'), explains quality scoring ('freshness/uptime/confidence'), and details citation contents ('source URL, license, SHA-256 hash'). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description appropriately supplements rather than contradicts them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: first states purpose and resources, second gives usage context and source, third details return format and components. Every sentence adds essential information without redundancy, making it front-loaded and zero-waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (simple fetch operation), rich annotations (read-only, non-destructive, etc.), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage, source details, and return format, leaving no gaps for the agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already fully documents both parameters (resource with enum values and id with omission behavior). The description doesn't add any parameter-specific semantics beyond what's in the schema, such as explaining resource differences or id ranges. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('fetch mock REST data') and resources ('posts, comments, users, and todos'), and distinguishes it from sibling tools by specifying it's for JSONPlaceholder mock data rather than real-world datasets like agriculture, consumer, or economic data. The mention of 'testing and prototyping' further clarifies its intended use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('useful for testing and prototyping') and implicitly suggests alternatives by noting it's for 'mock REST data' (implying real data tools might be better for production). However, it doesn't explicitly name specific sibling tools as alternatives or state when not to use it, keeping it at a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the source ('MangaDex (Public)'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality scores and citation information. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by filtering capabilities, return details, and source information. Every sentence adds value: the first defines the tool, the second lists filters, the third describes returns, and the fourth provides source and update info. It is efficiently structured with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple filters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage context, behavioral details like source and updates, and return format, making it sufficient for an agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, providing clear details for all 6 parameters. The description mentions filtering by 'title, content rating, tags, status, and demographics,' which aligns with parameters like title, contentRating, and status, but does not add significant meaning beyond what the schema already documents. With high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose as 'Search and discover manga titles from MangaDex — the world's largest manga library.' It specifies the verb ('search and discover'), resource ('manga titles'), and source ('MangaDex'), clearly distinguishing it from sibling tools which cover domains like agriculture, consumer, crime, etc. This is specific and avoids tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching manga with filters like title, content rating, tags, status, and demographics. It implicitly suggests usage for manga discovery tasks. However, it does not explicitly state when not to use it or name specific alternatives among siblings, which prevents a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations, including data source details (MusicBrainz, CC0 1.0 license, daily updates), return format (Katzilla envelope with data, quality, citation), and quality metrics (freshness/uptime/confidence). Annotations cover safety (readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true), so the description appropriately supplements without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, and the second details the return format and source information. Every sentence adds essential value with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple resource types), rich annotations, full schema coverage, and the presence of an output schema, the description is complete. It covers purpose, data source, update frequency, and return format, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents all parameters (resource, query, limit). The description does not add specific parameter semantics beyond implying search functionality, so it meets the baseline of 3 without compensating for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search the MusicBrainz open music encyclopedia') and resources ('artists, recordings, and releases'), distinguishing it from sibling tools by specifying its unique domain (music metadata) and data source (MusicBrainz).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search the MusicBrainz open music encyclopedia for artists, recordings, and releases'), but does not explicitly mention when not to use it or name alternative tools for similar purposes, such as other entertainment or culture-related siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the return format ('Returns questions with correct and incorrect answers'), data source details ('Source: Open Trivia Database (CC BY-SA 4.0), updates daily'), and output envelope structure ('Returns the Katzilla envelope { data, quality, citation }'). Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description enhances understanding without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by filtering details, return values, and source information in a compact two-sentence format. Every sentence adds value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (trivia data retrieval with filtering), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by context signals), the description is complete. It covers purpose, usage, behavioral traits, and output format, leaving no critical gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions filtering by category and difficulty, which aligns with the input schema parameters, but it does not add significant meaning beyond what the schema already provides (e.g., schema coverage is 100% with clear descriptions for amount, category, and difficulty). The baseline is 3 since the schema handles most parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get trivia questions') and resources ('from the Open Trivia Database'), and it distinguishes itself from siblings by specifying its unique domain (trivia) and data source, unlike other tools in the list (e.g., agriculture, crypto, or government tools).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning filtering options ('Filter by category and difficulty') and the data source, but it does not explicitly state when not to use it or name specific alternatives among siblings, such as other entertainment or reference tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the return format (Katzilla envelope with data, quality, citation), source details (PokéAPI, Public Domain, daily updates), and audit features (SHA-256 hash). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured into two sentences: one stating the purpose and return data, and another detailing the source and envelope format. Each sentence adds essential information without redundancy, making it front-loaded and appropriately sized for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no nested objects), rich annotations, and presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits, and return format, compensating adequately for any gaps. No additional explanation is needed for the agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'nameOrId' documented as 'Pokémon name or Pokédex ID.' The description mentions 'by name or ID' but does not add significant semantic details beyond the schema, such as format examples or constraints. Given high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Look up a Pokémon by name or ID using the PokéAPI.' It specifies the action ('look up'), resource ('Pokémon'), and method ('by name or ID'), clearly distinguishing it from sibling tools that handle different domains like agriculture, crime, or economics. The description is specific and avoids tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for retrieving Pokémon data via name or ID. However, it does not explicitly mention when not to use it or name alternatives among sibling tools, such as other fun-related tools like 'fun__swapi' for Star Wars data. The guidance is implied but lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source ('Radio Browser (Public Domain)'), update frequency ('updates daily'), and detailed return format ('Returns the Katzilla envelope { data, quality, citation }' with explanation of quality scores and citation components). Annotations already cover read-only, non-destructive, idempotent, and open-world characteristics, so the description appropriately supplements with operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: purpose and filtering, return details, and source/format information. Every sentence adds value without redundancy. It's appropriately sized for the tool's complexity and front-loads the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and an output schema (implied by the detailed return format description), the description provides excellent contextual completeness. It covers purpose, usage context, behavioral details, source attribution, and return format - everything needed for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all three parameters (name, limit, country). The description mentions filtering by 'name and country' which aligns with the schema but doesn't add significant semantic value beyond what's already in the structured fields. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search internet radio stations worldwide' with specific filtering capabilities ('Filter by name and country') and details what it returns ('station name, URL, codec, bitrate, and tags'). It distinguishes itself from sibling tools by focusing on radio station search, unlike other fun tools like chess, trivia, or gaming APIs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search internet radio stations worldwide') and implies usage for media/reference purposes. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, though the context suggests it's for radio station data specifically.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it discloses the source (RandomUser.me), license (Public Domain), update frequency (daily), and details about the output envelope (quality scores, citation with SHA-256 hash). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and optional filter, the second covers source, updates, and output format. Every sentence adds essential information with zero waste, making it highly concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral details (source, updates, output envelope), and aligns well with structured data, leaving no significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the 'count' and 'nationality' parameters. The description adds minimal param semantics by mentioning 'Optionally filter by nationality,' which aligns with the schema but doesn't provide extra details like example codes beyond what's implied. Baseline 3 is appropriate as the schema handles most of the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Generate random user profiles with names, addresses, emails, and photos.' It specifies the exact resource (user profiles) and key attributes, and distinguishes itself from siblings by mentioning its unique source (RandomUser.me) and output format (Katzilla envelope), making it highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it mentions optional filtering by nationality and specifies the source and update frequency (RandomUser.me, updates daily). However, it does not explicitly state when to use this tool versus alternatives or any exclusions, such as limitations compared to other demographic tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: 'Source: Rick and Morty API (Public Domain), updates daily' and details about the return envelope structure (Katzilla envelope with quality scores and citation). This provides freshness information and output format specifics that annotations don't cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: first sentence states purpose, second adds source and update frequency, third explains return format. Every sentence adds value with zero waste, and it's front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, read-only operation), rich annotations, and existence of an output schema, the description is complete. It covers purpose, source, update frequency, and return format details, compensating well where structured data might be insufficient (e.g., explaining the Katzilla envelope structure).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (id, name) well-documented in the schema. The description mentions 'by ID or search by name' but doesn't add significant semantic meaning beyond what the schema provides (e.g., no examples, format details, or search behavior nuances). This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Look up Rick and Morty characters by ID or search by name' - a specific verb (look up/search) and resource (characters). It distinguishes from sibling tools by specifying the Rick and Morty domain, which is unique among the listed siblings (e.g., fun__chess-com, fun__pokeapi).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'by ID or search by name' and notes that 'name overrides id if provided' (implied from input schema context). However, it doesn't explicitly state when to use this tool versus alternatives (e.g., other character lookup tools not present in siblings) or any exclusions, keeping it at a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits, but the description specifies the source ('SWAPI (Public Domain), updates daily') and return format ('Katzilla envelope { data, quality, citation }'), including details on quality scoring and citation components. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source and return format details. Every sentence adds value: the first defines the query, the second specifies the source and update frequency, and the third explains the return structure. It is efficiently structured with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (2 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return format, including quality and citation details. With an output schema present, it need not explain return values further, making this well-rounded for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for 'resource' (enum of types) and 'id' (resource ID). The description mentions querying 'people, planets, starships, and films,' aligning with the resource enum, but does not add further semantics beyond the schema, such as ID ranges or examples. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query the Star Wars API for people, planets, starships, and films. Returns detailed information from the Star Wars universe.' It specifies the verb ('query'), resources (people, planets, starships, films), and distinguishes itself from sibling tools by focusing on Star Wars data, unlike other fun__ tools like chess-com or pokeapi.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Query the Star Wars API for people, planets, starships, and films.' It implies this tool is for retrieving Star Wars universe data. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as fun__pokeapi for Pokémon data, though the Star Wars focus is distinct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source (U.S. Census Bureau), update frequency (annual), and details about the return structure (Katzilla envelope with quality scores and citation metadata). No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and output, the second adds crucial metadata about source, updates, and return format. Every element serves a clear purpose with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, high schema coverage, annotations present, and output schema exists), the description is complete. It covers purpose, source, update cadence, and return structure—adequately supplementing the structured data without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single 'address' parameter fully documented in the schema. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., format examples or constraints), so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Convert US addresses to latitude/longitude coordinates using the Census Bureau geocoder.' It specifies the verb ('convert'), resource ('US addresses'), and distinguishes from siblings by mentioning the specific data source (Census Bureau) and output format (Katzilla envelope).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for US addresses and returns specific census data (tract, block). However, it doesn't explicitly state when NOT to use it or name alternative geocoding tools among siblings (e.g., geo__geocode-xyz, geo__nominatim), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations: it specifies the source (country.is), update frequency (daily), and details about the output envelope (quality scores for freshness/uptime/confidence, citation with source URL, license, and SHA-256 hash). This enhances the agent's understanding of data reliability and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the output format and quality metrics. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by the description of the Katzilla envelope), the description is complete. It adequately explains the tool's function, source, update frequency, and output structure, providing all necessary context for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the schema fully documenting the single parameter 'ip' (type, optionality, and description). The description does not add any parameter-specific semantics beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the country for an IP address using country.is.' It specifies the verb ('Get'), resource ('country'), and scope ('IP address'), and distinguishes itself from siblings like geo__ipinfo or geo__geojs by naming the specific source (country.is) and output format (Katzilla envelope).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it mentions the source (country.is), update frequency (daily), and output structure. However, it does not explicitly state when to use this tool versus alternatives (e.g., geo__ipinfo for more detailed IP info) or any exclusions, leaving some room for improvement in sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the source ('geocode.xyz'), update frequency ('updates daily'), and details about the return structure ('Katzilla envelope') including quality metrics and citation info. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format. Every sentence adds value: the first states the action, the second specifies source and update frequency, and the third explains the output structure. It is appropriately sized with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It adds necessary context like source, update frequency, and output details, compensating well where structured data might lack.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'query' fully documented in the schema as 'Location to geocode'. The description does not add any additional meaning or syntax details beyond what the schema provides, such as examples or formatting requirements, so it meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Geocode a location query to get coordinates'), the resource ('using geocode.xyz'), and distinguishes it from siblings by specifying the source and data format ('Katzilla envelope'). It avoids tautology and provides distinct purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Geocode a location query to get coordinates'), mentions the source ('geocode.xyz (Free Tier)'), and implies usage for coordinate retrieval. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., geo__census-geocoder, geo__nominatim), which prevents a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the data source (GeoJS), update frequency ('updates daily'), and return structure ('Katzilla envelope { data, quality, citation }'), including details on quality metrics and citation contents. No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details in a structured manner. Every sentence adds value: source information, update frequency, return format, and quality/citation explanations. It is efficiently written with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter), rich annotations (covering safety and idempotency), and the presence of an output schema, the description is complete. It explains the tool's purpose, source, update frequency, and return structure, compensating for any gaps. No additional details are needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'ip' parameter fully documented in the schema ('IP address (omit for your own IP)'). The description does not add any parameter-specific semantics beyond what the schema provides, such as format examples or validation rules, so it meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Get geolocation data for an IP address using GeoJS.' It specifies the verb ('Get'), resource ('geolocation data'), and scope ('IP address'), and distinguishes itself from sibling tools (e.g., geo__ipinfo, geo__nominatim) by naming the specific API source (GeoJS).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Get geolocation data for an IP address using GeoJS' and notes the source is a 'Free API' that 'updates daily.' It implies this tool is for IP-based geolocation, but does not explicitly state when to use it versus alternatives like geo__ipinfo or geo__nominatim, nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the data source ('IPinfo'), update frequency ('updates daily'), and detailed return structure ('Katzilla envelope with quality scores and citation'), including audit features like SHA-256 hash. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format and audit features. Every sentence adds critical information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations covering safety and idempotency, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It adds necessary context like data source, update frequency, and return structure, compensating adequately where structured fields might lack detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'ip' parameter fully documented. The description does not add any parameter-specific details beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description relies on the schema for parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get geolocation data for an IP address'), resource ('using IPinfo'), and scope ('returns the Katzilla envelope'). It distinguishes from sibling tools by specifying its unique geolocation focus, unlike other geo tools like census-geocoder or nominatim that handle different geographic queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get geolocation data for an IP address') and mentions the data source and update frequency. However, it does not explicitly state when not to use it or name alternatives among sibling tools, such as geo__geojs or geo__technik-news-ip, which might offer similar IP geolocation services.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true), so the bar is lower. The description adds valuable context beyond annotations: it specifies the data source ('Pinball Map (Free API)'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation contents. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential contextual details (source, updates, return format). Every sentence adds value: the second sentence provides metadata, and the third explains the output structure. It is efficiently structured with zero wasted words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 optional parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, source, update frequency, and return format details, which complements the structured data well without needing to explain parameters or output values redundantly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the three parameters (lat, lon, distance). The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it mentions 'near coordinates' but doesn't clarify parameter interactions or defaults). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with specific verbs ('Find pinball machine locations near coordinates, or list all regions') and resources ('pinball machine locations', 'regions'). It clearly distinguishes this geography-focused tool from its many siblings across diverse domains like agriculture, consumer, crypto, etc., by specifying its unique geolocation function and data source (Pinball Map).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('Find pinball machine locations near coordinates, or list all regions'), implying usage for location-based queries. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., other geo__ tools like geo__census-geocoder or geo__nominatim), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source ('Postal Pincode API (Free API)'), update frequency ('daily'), and details about the return envelope structure (Katzilla format with quality scores and citation metadata), which helps the agent understand response format and data reliability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second explains the return format. Every sentence adds essential information without redundancy, making it easy to parse and front-loaded with key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, read-only), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context about data source, update frequency, and return structure, compensating for any gaps without over-explaining.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'pincode' fully documented in the schema as 'Indian postal pincode'. The description does not add further parameter details beyond what the schema provides, such as format examples or validation rules, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('look up'), resource ('Indian postal pincode details'), and scope ('Indian'). It distinguishes itself from sibling tools by specifying the geographic focus (India) and data type (postal pincodes), which none of the other geo__* tools explicitly cover.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for Indian postal pincodes, sourced from a specific API that updates daily. However, it does not explicitly state when not to use this tool or name alternative tools for similar purposes (e.g., other geographic lookup tools like geo__ziptastic for US ZIP codes), though the Indian focus implies differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the API source (TechnikNews), its free nature, daily update schedule, and details about the return structure (Katzilla envelope with quality scores and citation data). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), comprehensive annotations (read-only, non-destructive, etc.), and the presence of an output schema, the description is complete. It covers the source, update frequency, and return structure, which are crucial for an agent to use the tool effectively without needing to explain basic behaviors already encoded in structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'ip' parameter clearly documented. The description does not add any further details about parameter usage, syntax, or constraints beyond what the schema provides. Given the high schema coverage, a baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get IP geolocation data'), resource ('from TechnikNews API'), and scope ('Geography & Geolocation Agent'). It distinguishes itself from sibling tools by specifying the data source (TechnikNews) and the unique return format (Katzilla envelope), making its purpose explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by naming the source (TechnikNews Free API) and update frequency (daily), which helps determine when to use it. However, it does not explicitly state when not to use it or name alternatives (e.g., other geo tools like geo__ipinfo or geo__geojs), so it lacks full comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source ('USGS National Map'), update frequency ('daily'), and details about the return structure ('quality scores freshness/uptime/confidence; citation carries source URL, license, SHA-256 hash'), which helps the agent understand data reliability and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and source, the second details return format and quality metrics. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 simple parameters), rich annotations (covering safety and idempotency), and existence of an output schema, the description is complete. It adds necessary context about data source, update frequency, and return structure that complements the structured fields, ensuring the agent has sufficient information for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (lat, lon) fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, such as format examples or usage notes. According to guidelines, baseline is 3 when schema coverage is high (>80%).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get elevation data'), target resource ('for a geographic point'), and data source ('from the USGS National Map'), distinguishing it from sibling tools like 'geo__census-geocoder' or 'geo__nominatim' that handle different geographic functions. It avoids tautology by elaborating beyond the name/title.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying the data source, update frequency ('updates daily'), and return format ('Katzilla envelope'), but does not explicitly state when to use this tool versus alternatives (e.g., other elevation or geolocation tools) or any exclusions. This gives adequate guidance but lacks sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the data source (Ziptastic Free API), update frequency (daily), and detailed return structure (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enriches the agent's understanding of data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality and source, and the second details the return format and its components. Every sentence adds critical information (e.g., data source, update frequency, return envelope structure) without redundancy, making it front-loaded and zero-waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, read-only), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context like data source, update frequency, and return structure details, which complements the structured fields effectively without needing to explain basic operations or output values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a single parameter 'zipcode' documented as 'US zipcode'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples (e.g., 5-digit) or validation rules. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also doesn't need to given the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('city, state, and country for a US zipcode'), and distinguishes it from siblings by specifying its geographic focus and data source (Ziptastic). It explicitly mentions the return format, which helps differentiate it from other geo tools like geo__census-geocoder or geo__geocode-xyz.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get city, state, and country for a US zipcode') and implies limitations (US-only, daily updates). However, it does not explicitly state when not to use it or name specific alternatives among the many sibling tools, such as geo__census-geocoder for broader geocoding or geo__postal-pincode for non-US postal codes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and behavior. The description adds valuable context beyond annotations: it specifies the data source (Congress.gov), update frequency ('updates daily'), and details the return structure ('Katzilla envelope') with quality metrics and citation info, enhancing transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by optional filtering, source details, and return format explanation. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (read-only data retrieval), rich annotations (covering safety and behavior), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('limit' and 'congress') well-documented in the schema. The description mentions filtering 'by congress number' and implies result limiting, but does not add significant semantic details beyond what the schema provides. Baseline 3 is appropriate as the schema handles parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve recent bills'), resource ('from the U.S. Congress API'), and scope ('Optionally filter by congress number'). It distinguishes itself from sibling tools by specifying the exact data source (Congress.gov) and return format (Katzilla envelope), making its purpose unambiguous and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Retrieve recent bills') and mentions an optional filter ('by congress number'), but does not explicitly state when to use this tool versus alternatives or any exclusions. It implies usage for accessing congressional bill data, yet lacks explicit guidance on sibling tool comparisons or specific scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the Creative Commons Attribution license, daily update frequency, and details about the return format (Katzilla envelope with quality scores and citation metadata including SHA-256 hash). This provides important operational context not captured in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that pack substantial information: first sentence covers purpose, scope, and source; second sentence explains return format and metadata. Every element serves a clear purpose with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has comprehensive annotations, 100% schema coverage, and an output schema exists (implied by mention of Katzilla envelope), the description provides excellent contextual completeness. It covers data source, licensing, update frequency, and return format details that complement the structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all three parameters (query, organization, limit). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't provide additional parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Australian government open data portal'), identifies the resource ('data.gov.au'), and distinguishes it from siblings by specifying the geographic scope (Australia) and data source (government open data portal). It provides concrete details about dataset count, sources, and subject areas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool (searching Australian government open data) and mentions the data source and update frequency. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for similar data from other countries, though siblings like government__data-canada and government__data-uk exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the data source (avoindata.fi), update frequency ('updates daily'), license (CC-BY 4.0), and detailed return structure ('Katzilla envelope { data, quality, citation }' with quality metrics and citation details). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, and the second details the return format and source information. Every sentence adds critical value—no wasted words. It is front-loaded with the core functionality, making it easy for an agent to quickly grasp the tool's use.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with quality metrics), rich annotations, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, license, and return structure, compensating for any gaps. With annotations handling safety and idempotency, and the output schema likely detailing the envelope, no essential information is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (query and limit) fully described in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or limit usage tips. However, it implies the tool is for search operations, which aligns with the 'query' parameter. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search the Finnish open data portal (avoindata.fi).' It specifies the verb ('Search'), resource ('Finnish open data portal'), and scope ('Datasets from Finnish national and municipal authorities covering statistics, geospatial data, environment, transport, and education'). This distinguishes it from sibling tools like government__data-australia or government__data-uk by explicitly naming Finland and its data sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching Finnish open data across specific domains (statistics, geospatial, etc.). It does not explicitly state when not to use it or name alternatives among siblings, but the specificity of 'Finnish' and 'avoindata.fi' implies it should be used for Finland-specific queries rather than other countries' data tools listed as siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source (data.gouv.fr), update frequency (daily), license information (Licence Ouverte / Open Licence 2.0), and details about the return structure (Katzilla envelope with quality scores and citation data).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with zero wasted sentences. It front-loads the core purpose, provides essential context about the data source and scope, and concludes with important behavioral details about returns. Every sentence serves a clear purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's search functionality, comprehensive annotations (including readOnlyHint, idempotentHint, openWorldHint), 100% schema coverage, and the presence of an output schema, the description provides complete contextual information. It covers the data source, scope, update frequency, licensing, and return structure, making it fully adequate for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all three parameters (query, organization, limit). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation without providing extra semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), the target resource ('French government open data portal'), and the scope ('Over 45,000 datasets from French national and local government agencies'). It clearly distinguishes this from sibling tools by specifying the French government data source, unlike other government data tools like 'government__data-australia' or 'government__data-uk'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool: for searching French government open data across multiple domains (demographics, transport, environment, health). It doesn't explicitly state when not to use it or name specific alternatives, but the context is sufficient to guide usage decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the update frequency ('updates daily'), describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality scoring components ('freshness/uptime/confidence') and citation details ('source URL, license, SHA-256 hash'). Annotations cover safety (readOnly, non-destructive, idempotent, openWorld), so the description appropriately supplements with operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and source, the second details the return format and its components. Every element adds value without redundancy, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters), rich annotations (readOnly, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, return format, and quality metrics, leaving detailed output structure to the output schema as appropriate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (query and limit). The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3. The description focuses on tool behavior rather than parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Gdansk open data portal for public datasets'), identifies the resource ('Gdansk open data portal'), and distinguishes it from siblings by specifying the geographic scope (Gdansk, Poland) and data source (Multimedia Gdansk Open Data). This is more precise than generic search tools in the list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search the Gdansk open data portal for public datasets'), but does not explicitly state when not to use it or name specific alternatives among the many sibling tools. It implies usage for Gdansk-specific data, but lacks explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the source (GovData.de), update frequency (daily), and details the return format (Katzilla envelope with data, quality scores, and citation including SHA-256 hash). Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, so the description complements these with operational details without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source and return details in two efficient sentences. Every sentence adds value: the first defines the search scope, and the second explains the output structure and quality metrics, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the description of the Katzilla envelope), the description is complete. It covers purpose, source, update frequency, and return format, adequately supplementing the structured data without needing to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents the parameters (query, groups, limit) with descriptions and examples. The description does not add further meaning or syntax details for the parameters, such as explaining 'groups' categories beyond the schema's example. Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the German government open data portal (GovData) for datasets from federal, state, and municipal authorities, covering specific domains like statistics, geospatial data, environment, transport, and public services. It distinguishes itself from siblings by specifying the German focus and GovData source, unlike other government data tools (e.g., data-australia, data-canada).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching German open data across various categories, but does not explicitly state when to use this tool versus alternatives or provide exclusions. It mentions the source (GovData.de) and daily updates, which gives context, but lacks direct comparison to sibling tools like data-france or data-uk for guidance on regional selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it discloses the data source (Data.gov, Public Domain), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation information including SHA-256 hash). This significantly enhances understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: purpose statement, source/update info, and return format details. Every sentence adds value without redundancy. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has comprehensive annotations, 100% schema coverage, and an output schema exists, the description provides excellent contextual completeness. It covers purpose, source, update frequency, and return format details, which complements the structured data well. No significant gaps remain for this search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (query, limit) well-documented in the schema. The description doesn't add any meaningful parameter semantics beyond what the schema already provides. It mentions searching but doesn't elaborate on query syntax or limit implications. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the U.S. federal data catalog on Data.gov for open government datasets'), identifies the resource ('datasets'), and distinguishes it from siblings by specifying the source (Data.gov) and scope (U.S. federal data). This is more specific than generic search tools in the list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search the U.S. federal data catalog on Data.gov for open government datasets'), but doesn't explicitly mention when not to use it or name specific alternatives among the many sibling tools. It implies usage for U.S. federal data searches without stating exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it specifies the data source ('Data.gov.ie'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation components. This enhances understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every sentence adds essential information without waste, and it is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with two parameters), rich annotations (four hints), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete enough. It covers purpose, source, update frequency, and return structure, compensating well for any gaps without needing to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('query' and 'limit') fully documented in the schema. The description does not add any additional meaning or details about the parameters beyond what the schema provides, such as query syntax examples or limit rationale. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Irish government open data portal for public datasets'), identifies the resource ('Irish government open data portal'), and distinguishes it from siblings by specifying the source ('Data.gov.ie') and geographic focus ('Irish'), which differentiates it from other government data tools like data-australia or data-canada.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search the Irish government open data portal for public datasets'), but does not explicitly state when not to use it or name specific alternatives among the many sibling tools. It implies usage for Irish open data searches without exclusions or comparisons to similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations by specifying the update frequency ('updates daily'), describing the return format ('Katzilla envelope { data, quality, citation }'), and detailing quality metrics ('freshness/uptime/confidence') and citation components ('source URL, license, SHA-256 hash'). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured into two sentences: one stating the tool's purpose and source, and another detailing the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search functionality with quality metrics), rich annotations, 100% schema coverage, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, leaving no significant gaps for the agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('query' and 'limit'). The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or limit usage guidance. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Istanbul Metropolitan Municipality (IBB) open data portal for public datasets'), identifies the resource ('public datasets'), and distinguishes it from siblings by specifying the source ('Istanbul Metropolitan Municipality (Open Data)'). It avoids tautology and provides a precise verb+resource combination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly provides usage context by stating the source and update frequency ('updates daily'), which helps determine when to use it. However, it does not explicitly mention when not to use it or name alternatives among the many sibling tools, such as other government data tools like 'government__data-uk' or 'government__datagov'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (dati.gov.it), license (Italian Open Data Licence 2.0), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details). While annotations cover read-only, non-destructive, idempotent, and open-world characteristics, the description provides practical implementation details that help the agent understand what to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with zero wasted words. It front-loads the core purpose, then provides essential context about source, license, updates, and return format. Every sentence adds value, and the information density is high without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with quality scoring), rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and existence of an output schema, the description provides excellent contextual completeness. It covers source, license, update frequency, and return structure, making it fully sufficient for an agent to understand when and how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (query and limit). The description doesn't add specific parameter semantics beyond what's in the schema, such as query syntax examples or limit implications. The baseline score of 3 reflects adequate parameter documentation through the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search'), target resource ('Italian government open data portal'), and scope ('datasets from Italian national and regional agencies covering economy, environment, society, transport, and culture'). It distinguishes itself from siblings by specifying the Italian focus and dati.gov.it source, unlike other government data tools like data-australia or data-canada.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching Italian government open data across multiple domains. It implicitly distinguishes from siblings by specifying the Italian focus, but does not explicitly state when not to use it or name specific alternative tools for other regions or data types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source, update frequency (daily), and details about the return structure (quality scores for freshness/uptime/confidence, citation with URL, license, and SHA-256 hash). This enriches the agent's understanding of the tool's behavior and output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by source details and return format explanation. Every sentence adds value: the first defines the action, the second provides context (source and updates), and the third clarifies the output structure. There is no wasted text, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters and structured output), the description is complete. It covers purpose, source, update frequency, and detailed return format. With annotations covering safety and idempotency, and an output schema existing (though not provided here), the description provides sufficient context for an agent to use the tool effectively without needing to explain return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (query and limit). The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or limit usage tips. Thus, it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search the Lviv city open data portal for public datasets') and resources ('public datasets'), and distinguishes it from siblings by specifying the geographic scope (Lviv, Ukraine) and data source (Lviv City Administration Open Data). It also mentions the return format (Katzilla envelope), which helps differentiate it from other data search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching Lviv's open data portal with daily updates. However, it does not explicitly state when not to use it or name alternatives among the many sibling tools (e.g., other government data tools for different regions like data-germany or data-uk), which would be needed for a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it specifies the data source URL, update frequency ('updates daily'), license information ('CC0 / Public Domain'), and details about the return format ('Katzilla envelope { data, quality, citation }') with quality metrics and citation components. This enhances understanding of the tool's behavior without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and data scope; the second details return format and metadata. Every sentence adds critical information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has annotations covering safety and idempotency, a rich output schema (implied by the description of the Katzilla envelope), and 100% schema coverage for inputs, the description is complete. It adds necessary context about the data source, update frequency, license, and return structure, compensating well for any gaps not covered by structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (query and limit) fully described in the schema. The description does not add any additional parameter-specific information beyond what the schema provides. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Search the Dutch government open data portal (data.overheid.nl)' with specific resources ('Datasets from national and local Dutch government agencies covering demographics, environment, geospatial data, and public services'). It clearly distinguishes from siblings by specifying the Dutch government data source, unlike other government data tools (e.g., data-australia, data-canada).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching Dutch government open data. It mentions the source (data.overheid.nl) and data types covered. However, it does not explicitly state when not to use it or name specific alternative tools for similar data from other countries, though the sibling list shows many country-specific data tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the source (dane.gov.pl), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation components. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, source, and updates; the second explains the return format and its components. Every sentence adds critical information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with structured output), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It adds necessary context like data source, update frequency, and return format details, compensating adequately where structured fields may not fully convey usage nuances.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('query' and 'limit'). The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or result formatting details. Given the high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search'), resource ('Polish open data portal (dane.gov.pl)'), and scope ('Datasets from Polish national and regional agencies covering public finance, demographics, education, health, and environment'). It distinguishes itself from sibling tools by specifying the Polish data source, unlike other government data tools like 'government__data-australia' or 'government__data-canada'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool by specifying the data source (dane.gov.pl) and subject areas (public finance, demographics, etc.). However, it does not explicitly state when not to use it or name alternative tools for similar data from other countries, which would be needed for a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations: it specifies the data source ('dados.gov.pt'), update frequency ('updates daily'), licensing ('CC-BY 4.0'), and detailed return format ('Katzilla envelope { data, quality, citation }' with quality metrics and citation details). While annotations cover safety (readOnlyHint=true, destructiveHint=false), the description provides practical implementation details that help the agent understand what to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: purpose statement, data scope and source, and return format details. Every sentence adds essential information without redundancy. It's front-loaded with the core functionality and maintains appropriate density throughout.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with quality scoring), rich annotations, complete input schema, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, scope, source, licensing, update frequency, and detailed return format - everything needed to understand the tool's behavior without needing to explain basic parameters or output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (query and limit). The description doesn't add any parameter-specific information beyond what's in the schema, such as query syntax examples or result formatting details. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose as 'Search the Portuguese open data portal (dados.gov.pt)' with specific resources ('Datasets from Portuguese national and local government agencies') and domains ('economy, health, education, transport, and environment'). It clearly distinguishes itself from sibling tools by focusing exclusively on Portuguese government data, unlike other government__data-* tools for different countries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Search the Portuguese open data portal') and what it covers ('Datasets from Portuguese national and local government agencies'). However, it doesn't explicitly mention when NOT to use it or name specific alternative tools for similar data from other countries, though the sibling list shows many country-specific alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations: it specifies the data source ('Queensland Government (Open Data)'), update frequency ('daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation components. This enhances understanding of the tool's behavior without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second explains the return format. Every sentence adds value without redundancy, making it easy to parse and front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with structured returns), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, leaving detailed output to the schema. No gaps are evident for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (query and limit). The description does not add any additional semantic information about parameters beyond what the schema provides, such as query syntax examples or limit usage tips. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search'), target resource ('Queensland government open data portal for public datasets'), and scope ('open data portal'). It distinguishes from siblings by specifying the Queensland government source and daily updates, unlike broader tools like government__data-australia or other regional datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching Queensland government open data. It does not explicitly state when not to use it or name alternatives, but the specificity implies it's for Queensland-focused queries rather than other regions or general searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the source (data.gov.uk), update frequency (daily), license (Open Government Licence), and details about the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). This enriches the agent's understanding of data provenance and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, and source; the second explains the return format and its components. Every sentence adds critical information without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple parameters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers the tool's purpose, data source, update frequency, license, and output structure, providing all necessary context for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the three parameters (query, publisher, limit). The description does not add any parameter-specific details beyond what the schema provides, such as example queries or publisher formats. However, it implies search functionality through 'Search the UK government open data portal,' which aligns with the parameters but doesn't enhance their semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search the UK government open data portal (data.gov.uk).' It specifies the resource ('over 50,000 datasets from UK central and local government') and lists key domains covered (transport, health, education, etc.), distinguishing it from sibling tools like government__data-australia or government__data-canada by focusing on UK-specific data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching UK government open data, with daily updates and an Open Government Licence. It does not explicitly mention when not to use it or name specific alternatives among siblings, but the domain focus implies usage for UK-related queries rather than other countries' data portals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the source ('datos.gob.es'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope with quality scores and citation info including SHA-256 hash'). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by source details and return format specifics. Every sentence adds value: the first defines the action, the second provides source context, and the third explains the output structure. There is no wasted text, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and output details, providing sufficient context for an agent to use the tool effectively without needing to explain return values redundantly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for 'query' and 'limit' parameters. The description does not add any additional semantic details about the parameters beyond what the schema provides, such as query syntax examples or limit usage context. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Spanish government open data portal'), the resource ('public datasets'), and the source ('datos.gob.es'). It distinguishes from siblings by specifying the Spanish government context, unlike other government tools like 'government__data-australia' or 'government__data-canada'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: searching for public datasets from the Spanish government open data portal. It mentions the source and update frequency ('updates daily'), which helps guide usage. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as other country-specific data portals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. While annotations indicate read-only, non-destructive, idempotent, and open-world traits, the description specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 data hash'), which are not covered by annotations. No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential details about the source, update frequency, and return format in a compact manner. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple filters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers the purpose, data source, behavioral traits, and output structure, leaving no significant gaps for an agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all 5 parameters (query, office, cycle, page, limit) with descriptions, enums, and constraints. The description does not add any parameter-specific semantics beyond what the schema provides, such as example queries or advanced usage tips, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search for') and resource ('U.S. federal election candidates from the Federal Election Commission (FEC) API'), distinguishing it from sibling tools that focus on other government or public data domains like agriculture, crime, or economic data. It explicitly identifies the data source and scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by specifying the data source (FEC API) and update frequency (daily), which helps users understand when to use this tool for current federal election candidate data. However, it does not explicitly mention when not to use it or name specific alternatives among the many sibling tools, such as other government data tools like 'government__congress-bills' or 'government__sec-edgar'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it discloses the data source ('GovInfo (GPO) (Public Domain)'), update frequency ('updates daily'), and detailed return format ('Katzilla envelope { data, quality, citation }' with quality metrics and citation details including SHA-256 hash). This significantly enhances understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every element adds value without redundancy. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 optional parameter, rich annotations, output schema exists), the description is complete. It covers purpose, source, update frequency, and detailed return format. With annotations handling safety/behavioral aspects and an output schema presumably documenting the return structure, no additional information is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage with a clear parameter description for 'limit'. The description doesn't add any parameter-specific information beyond what's in the schema, which is acceptable given the high schema coverage. The baseline score of 3 is appropriate when the schema adequately documents parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List available document collections'), resource ('from the U.S. Government Publishing Office (GPO) GovInfo API'), and scope ('available document collections'). It distinguishes itself from sibling tools like 'govinfo-content', 'govinfo-granule', 'govinfo-package', and 'govinfo-search' by focusing on listing collections rather than content retrieval or search operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('List available document collections'), but does not explicitly state when not to use it or name specific alternatives. The sibling tool list includes related govinfo tools (e.g., govinfo-search for searching within collections), but the description doesn't explicitly guide the agent to choose between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains what quality scores measure ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 data hash'). While annotations cover read-only, non-destructive, idempotent, and open-world aspects, the description provides important implementation details about data structure and audit capabilities.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and scope with examples, the second details return format and source information. Every element serves a clear purpose with zero redundant information, making it easy to parse while being comprehensive.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations, complete parameter documentation, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, usage context, return format details, source attribution, and data quality aspects - everything needed for effective tool selection and invocation without duplicating structured field information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters. The description doesn't add any additional parameter semantics beyond what's in the schema descriptions, though it does provide context about where granuleId values come from ('from govinfo-search or the package's granules listing'), which is marginally helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Fetch the metadata summary'), resource ('single granule'), and scope ('sub-document within a GovInfo package') with concrete examples (CFR section, Federal Register notice). It explicitly distinguishes this from sibling tools by focusing on individual granules rather than packages or searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('for a single granule') and implies usage after obtaining granule identifiers from other tools ('from govinfo-search or the package's granules listing'). However, it doesn't explicitly state when NOT to use it or name specific alternative tools for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the source URL (podatki.gov.si), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation data including SHA-256 hash). While annotations already indicate read-only, non-destructive, idempotent, and open-world characteristics, the description enriches this with practical implementation details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and source, the second details the return format and its components. Every element serves a clear purpose with zero wasted words, making it easy to parse while being information-dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and the presence of an output schema, the description provides excellent contextual completeness. It covers purpose, source, update frequency, and return format details that complement the structured metadata effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (query and limit). The description doesn't add any additional parameter semantics beyond what's in the schema, so it meets the baseline expectation without providing extra value regarding parameter usage or meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Slovenian government open data portal'), the resource ('public datasets'), and distinguishes it from siblings by specifying the exact source (podatki.gov.si) and data domain (Slovenian government open data). It goes beyond generic search by identifying the unique portal and data type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Search the Slovenian government open data portal for public datasets') and mentions the source and update frequency. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for similar data from other countries, though the sibling list shows many government data tools for different regions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a read-only, non-destructive, idempotent, and open-world operation. The description adds valuable context beyond this: it specifies the data source ('ReceitaWS'), update frequency ('daily'), rate limits implied by 'Free Tier', and the return format ('Katzilla envelope') with details on quality metrics and citation. This enhances the agent's understanding of reliability and auditability, though it doesn't detail exact rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second explains the return format and its components. Every sentence adds critical information without redundancy, making it easy for an agent to parse and apply.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, read-only operation) and the presence of annotations and an output schema, the description is complete. It covers purpose, source, update frequency, return format, and data quality—addressing all necessary context for an agent to use the tool effectively without needing to explain basic parameters or output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the 'cnpj' parameter with format examples. The description does not add any parameter-specific semantics beyond what the schema provides, such as validation rules or usage tips. With high schema coverage, the baseline score of 3 is appropriate, as the description relies on the schema for parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Look up Brazilian company information'), the resource ('by CNPJ number'), and the source ('using the ReceitaWS API'). It distinguishes itself from siblings by focusing on Brazilian company data, unlike other government tools that target different countries or data types (e.g., 'government__congress-bills' for US legislation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for Brazilian company lookups via CNPJ. It implies an alternative by mentioning the 'Free Tier' and 'updates daily', suggesting limitations that might require other sources for more frequent or comprehensive data. However, it does not explicitly name alternative tools or specify when not to use it, such as for non-Brazilian entities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the bar is lower. The description adds valuable context beyond annotations: it specifies the data source (GSA Site Scanning), update frequency ('updates daily'), and details the return structure ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation contents. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first, followed by source details and return structure. Every sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (read-only data access with two parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the description of the return structure), the description is complete enough. It explains the data source, update frequency, and return format, compensating well for any gaps and aligning with the structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing full documentation for both parameters (limit and target_url). The description does not add any parameter-specific semantics beyond what the schema already states, such as explaining how target_url filtering works or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Access GSA Site Scanning data') and resources ('federal website health checks'), and distinguishes it from siblings by specifying the data source (GSA Site Scanning) and content types (DAP analytics, USWDS adoption, mobile readiness). It goes beyond a tautology by detailing what the data encompasses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Access GSA Site Scanning data for federal website health checks'), but does not explicitly state when not to use it or name alternatives among the sibling tools. It implies usage for federal website health data, but lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source (Tenders.guru, Open Data), update frequency (daily), and details about the return structure (Katzilla envelope with quality scores and citation info including SHA-256 hash for audit). This enriches the agent's understanding of data freshness, reliability, and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, with every sentence earning its place. It efficiently covers purpose, scope, source, update frequency, and return format in two sentences, with no redundant or vague language. The structure moves from general function to specific details logically.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context about data source, freshness, and return structure that complements the structured fields, making it fully adequate for agent use without over-explaining.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (limit and country) well-documented in the schema. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain how country filtering works or provide examples beyond the schema's ISO code description). Since the schema carries the full burden, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search public procurement tenders'), resource ('from Tenders.guru'), and scope ('Supports multiple countries'). It distinguishes itself from sibling tools by specifying its unique data domain (government tenders) and source (Tenders.guru), unlike other government tools that focus on different data types like bills, spending, or regulations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching public procurement tenders from a specific source (Tenders.guru) across multiple countries. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., other government data tools like government__datagov or government__usaspending), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description adds valuable context beyond that: it specifies the data source (data.police.uk), update frequency ('updates daily'), license (Open Government Licence), and details about the return format (Katzilla envelope with quality scores and citation). This enhances understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format explanation in a compact two-sentence structure. Every sentence adds value (e.g., data source, update frequency, return envelope details) with zero waste, making it highly efficient and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (querying crime data with parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by return format details), the description is complete. It adds necessary context like source, license, update frequency, and return structure, compensating well for any gaps and ensuring the agent has sufficient information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (lat, lng, date) with defaults and descriptions. The description adds no additional parameter semantics beyond implying location and date filtering, which is already covered. Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query street-level crime data') and resources ('UK Police API by location and date'), distinguishing it from siblings by specifying the exact data source and scope. It explicitly mentions the source (data.police.uk) and the type of data returned, making it distinct from other tools in the list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('by location and date') and mentions the data source and update frequency ('updates daily'), which helps in understanding when to use it. However, it does not explicitly state when not to use this tool or name specific alternatives among siblings, though the context implies it's for UK crime data queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source (U.S. Office of Personnel Management — USAJobs), update frequency ('updates daily'), and return format details (Katzilla envelope with quality scores and citation metadata). Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description appropriately supplements with operational specifics without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and filters, and the second covers source, updates, and return format. Every element adds value (e.g., source credibility, update frequency, output structure), with no redundant or verbose phrasing, making it front-loaded and highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits (source, updates), and output expectations, leaving no gaps for the agent to operate effectively without over-explaining structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all four parameters (keyword, locationName, payGradeFrom, limit). The description mentions filtering by 'keyword, location, salary, and agency', which aligns with but does not add meaning beyond the schema—salary maps to payGradeFrom, and agency is not a parameter. Thus, it meets the baseline for high schema coverage without significant enhancement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search') and resource ('current federal government job openings from USAJobs.gov'), and distinguishes it from siblings by specifying its unique domain (federal jobs) and source (USAJobs.gov). It explicitly identifies the agent context ('Government & Public Data Agent'), making its role unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (searching federal job openings) and lists filterable parameters (keyword, location, salary, agency), but does not explicitly mention when not to use it or name alternative tools for non-federal job searches or other government data. The sibling list includes many government data tools, but no direct alternatives for job searches are highlighted.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source (USAspending.gov), update frequency (daily), return format (Katzilla envelope with quality scores and citation details), and audit features (SHA-256 hash). No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the tool's purpose and return data, and the second adds source, update frequency, and output format details. Every sentence provides essential information without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adequately explains the tool's purpose, data source, update behavior, and output structure, leaving no significant gaps for an agent to understand its use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the 'limit' parameter with its constraints and default. The description does not add any parameter-specific information beyond what the schema provides, such as how limit interacts with pagination or data volume. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List federal agencies'), resource ('from the USAspending.gov API'), and scope ('Returns agency names, budgets, and identifiers'). It distinguishes itself from sibling tools by focusing exclusively on federal agencies from a specific government data source, unlike tools like 'government__congress-bills' or 'government__fec-candidates' which target different government data domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: when needing federal agency data from USAspending.gov, with daily updates, and a specific return format (Katzilla envelope). However, it does not explicitly state when not to use it or name alternative tools for similar data (e.g., other government data tools), though the sibling list shows many distinct domains.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the real-time update nature, the specific return format (Katzilla envelope with data, quality, citation), and details about quality scores and citation contents (source URL, license, SHA-256 hash). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description effectively supplements with operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by filter details, source information, and return format specifics. Every sentence adds value without redundancy, and it efficiently conveys necessary information in a compact form, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, non-destructive, idempotent, open-world), 100% schema coverage, and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits, and return format, leaving no significant gaps for the agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions filter parameters (state, year, number of results), but the input schema already provides 100% coverage with detailed descriptions for each parameter. The description does not add significant semantic meaning beyond what the schema specifies, such as explaining interactions between parameters or typical use cases, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query FEMA disaster declaration summaries') and resources ('FEMA disaster declaration summaries'), and distinguishes it from sibling tools by specifying its unique domain (hazards/disasters) and data source (FEMA). It avoids tautology by explaining what the tool does beyond the name/title.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying filter parameters (state, year, number of results) and the real-time nature of updates. However, it does not explicitly state when not to use this tool or name specific alternatives among the sibling tools (e.g., hazards__fema-nfip-claims), which prevents a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it discloses the return format (Katzilla envelope with data, quality, citation), explains quality scoring (freshness/uptime/confidence), and details citation contents (source URL, license, SHA-256 hash). This enriches the agent's understanding of the tool's output and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: it opens with the core purpose, lists key data fields, states usage context, specifies source and update frequency, and details the return format—all in three dense sentences with zero wasted words. Each sentence adds critical information, making it highly concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (data retrieval with quality metadata), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, usage, source, update cadence, and detailed output behavior, leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter (state, year, limit) well-documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining interactions between parameters or default behaviors. Baseline 3 is appropriate when the schema fully covers parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: retrieving 'National Flood Insurance Program (NFIP) claims data from FEMA' with specific data fields listed (loss amounts, property types, flood zones, dates of loss). It clearly distinguishes from sibling tools like 'hazards__fema-disasters' by focusing on flood insurance claims rather than disaster declarations or other hazard data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('useful for flood risk assessment and disaster analysis') and specifies the data source and update frequency. However, it does not explicitly state when to use this tool versus alternatives (e.g., other FEMA or hazard-related tools) or any prerequisites, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source (NOAA National Weather Service), update frequency (real-time), return format (Katzilla envelope with quality scores and citation details), and that it includes specific alert types (hurricane warnings, watches, tropical storm warnings). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: first stating the tool's purpose and scope, then detailing the return format and data quality. Every sentence adds essential information with zero wasted words, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single optional parameter), rich annotations (four hints), and existence of an output schema, the description is complete. It covers purpose, source, update frequency, return format, and data quality aspects, providing all necessary context for an agent to use the tool effectively without needing to explain return values (handled by output schema).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter ('limit') fully documented in the schema. The description doesn't mention any parameters, which is acceptable since the schema provides complete documentation. The baseline score of 3 reflects adequate parameter semantics when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get active hurricane and tropical storm alerts from the National Weather Service API.' It specifies the resource (hurricane/tropical storm alerts), source (NOAA National Weather Service), and distinguishes from siblings like 'hazards__nws-alerts' by focusing specifically on hurricanes/tropical storms rather than general weather alerts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for real-time hurricane/tropical storm alerts. It mentions the source and update frequency, but doesn't explicitly state when not to use it or name specific alternatives among siblings (e.g., 'hazards__nws-alerts' for broader weather alerts).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source ('NASA FIRMS'), update frequency ('real-time'), and output structure ('Returns the Katzilla envelope { data, quality, citation }'). Annotations already indicate it's read-only, non-destructive, idempotent, and open-world, but the description enhances this with details on data freshness, quality scoring, and audit features like the SHA-256 hash, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: it starts with the core purpose, adds source and update details, and concludes with the return format—all in two sentences with no redundant information. Every sentence adds value, such as clarifying the output envelope and its components, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (real-time satellite data retrieval), rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema, the description is complete. It covers the purpose, source, update behavior, and output structure, compensating for any gaps. With annotations and output schema handling safety and return values, the description provides sufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with each parameter (west, south, east, north, days) clearly documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining how the bounding box works or the implications of the 'days' parameter on data retrieval. This meets the baseline score of 3 since the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get active fire/hotspot data from NASA FIRMS (Fire Information for Resource Management System) using VIIRS satellite imagery.' It specifies the verb ('Get'), resource ('active fire/hotspot data'), source ('NASA FIRMS'), and methodology ('VIIRS satellite imagery'), distinguishing it from sibling tools like 'hazards__fema-disasters' or 'hazards__usgs-earthquakes' which cover different hazard types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for real-time wildfire data from a specific NASA system, implying it's suitable for environmental monitoring or disaster response. However, it does not explicitly state when not to use it or name alternative tools for similar data, such as other hazard-related siblings like 'environment__copernicus' or 'hazards__hurricane-tracking', which could help differentiate further.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (National Weather Service, Public Domain), update frequency (real-time), and return format (Katzilla envelope with quality scores and citation details), which helps the agent understand data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by filtering options, source details, and return format in two efficient sentences. Every sentence adds value without redundancy, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, usage context, behavioral traits (source, updates, return format), and does not need to explain return values since an output schema exists. No gaps are present for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters (area, severity, urgency, limit). The description mentions filtering by state, severity, and urgency, which aligns with the schema but does not add significant semantic details beyond what the schema provides, such as explaining parameter interactions or default behaviors.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Get active weather alerts') and resource ('from the National Weather Service'), and distinguishes itself from sibling tools by specifying its domain (hazards) and data source (NWS), unlike other tools in the list (e.g., hazards__fema-disasters, hazards__usgs-earthquakes).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Filter by state, severity, and urgency') and implies when to use it (for real-time weather alerts), but does not explicitly state when not to use it or name alternatives among sibling tools (e.g., hazards__fema-disasters for disaster data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source ('USGS Water Services (Public Domain)'), update frequency ('updates real-time'), and return format ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation content. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format. Every sentence adds value: the first states what it does, the second gives source and update info, and the third explains the return structure. It is efficiently structured with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, behavior, and output format, compensating well for any gaps. No additional explanation is needed for parameters or safety due to schema and annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters (sites, parameterCd, period). The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it mentions 'streamflow, gage height' but the schema already explains parameter codes). Baseline score of 3 is appropriate as the schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get real-time water data (streamflow, gage height) from USGS water monitoring sites.' It specifies the action ('Get'), resource ('real-time water data'), and scope ('USGS water monitoring sites'), and distinguishes itself from sibling tools in the hazards category (e.g., hazards__fema-disasters, hazards__usgs-earthquakes) by focusing on water data rather than other hazards.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for real-time water data from USGS, with a specified source and update frequency. However, it does not explicitly state when not to use this tool or name alternatives among siblings (e.g., hazards__usgs-earthquakes for earthquake data), though the context implies it's for water-related queries. This is good but lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond these: it specifies the data source ('CDC / NCHS'), update frequency ('updates daily'), return format ('Katzilla envelope { data, quality, citation }'), and details about quality scoring and citation contents. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose, dataset, and filters; the second details the return format, quality metrics, and citation. Every phrase adds value (e.g., source attribution, update frequency, output structure), with no redundant or vague language. It is front-loaded with core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, read-only query), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage context, behavioral details (source, updates, return format), and output semantics, leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all three parameters (limit, year, cause) well-documented in the schema. The description mentions filtering 'by year, cause, and state' (though 'state' is not a parameter in the schema, which is a minor discrepancy). It adds minimal semantic value beyond the schema, such as example causes ('e.g., Heart disease'), but the schema already provides similar context. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Query CDC open data for leading causes of death in the United States by year, cause, and state.' It specifies the verb ('query'), resource ('CDC open data'), dataset ('NCHS Leading Causes of Death'), and scope ('United States'), clearly distinguishing it from sibling tools like health__cdc-data or health__who-gho which cover different health datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for querying leading causes of death data from the CDC's NCHS dataset. It mentions filtering capabilities ('by year, cause, and state') and the data source/update frequency. However, it does not explicitly state when not to use it or name specific alternatives among the many sibling health tools (e.g., health__cdc-data for other CDC datasets).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, indicating safe, repeatable, and open-ended queries. The description adds valuable context beyond annotations: it specifies the data source (CMS, Public Domain), update frequency (quarterly), and the return format (Katzilla envelope with data, quality scores, and citation details like SHA-256 hash). This enhances transparency about data freshness, auditability, and output structure without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, datasets, source, and updates; the second explains the return format and audit features. Every sentence adds value, with no redundant information, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, annotations, and an output schema), the description is complete. It covers the purpose, data source, update frequency, and return format. With annotations handling safety and idempotency, and an output schema presumably detailing the Katzilla envelope, the description provides sufficient context without needing to explain return values or behavioral traits exhaustively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear enum values and defaults for parameters like 'dataset' and 'state'. The description does not add significant meaning beyond the schema, as it only mentions the datasets generically (e.g., 'hospital quality ratings') without detailing parameter interactions or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description provides minimal extra parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it searches Medicare provider data from CMS, specifying the datasets (hospital quality ratings, nursing home inspections, physician comparisons, ACO performance) and the source. It uses a specific verb ('search') and resource ('CMS data catalog'), distinguishing it from sibling tools in the health category like 'health__cdc-data' or 'health__fda-recalls' by focusing on provider data rather than disease or regulatory information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning the data source (CMS), update frequency (quarterly), and the types of datasets available. However, it does not explicitly state when to use this tool versus alternatives (e.g., other health data tools like 'health__cdc-data' for disease data) or any prerequisites. The guidance is clear but lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations: it discloses the data source ('disease.sh (Open Data)'), update frequency ('updates daily'), and output structure ('Katzilla envelope { data, quality, citation }'). The annotations already indicate it's read-only, non-destructive, idempotent, and open-world, so the description complements this with practical details without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second explains the output format and additional metadata. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 optional parameter, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and output structure, which, combined with the structured fields, provides all necessary context for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'country' documented as 'Country name or ISO code; omit for global stats.' The description does not add further parameter details beyond what the schema provides, such as examples or constraints, so it meets the baseline of 3 where the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get global or country-level COVID-19 statistics from the disease.sh API.' It specifies the verb ('Get'), resource ('COVID-19 statistics'), and scope ('global or country-level'), and distinguishes itself from siblings by focusing on COVID-19 data from a specific source, unlike other health tools that cover different datasets (e.g., CDC data, FDA recalls).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it specifies the data source (disease.sh API), update frequency (daily), and scope (global or country-level). However, it does not explicitly state when to use this tool versus alternatives (e.g., other health tools like 'health__cdc-data' or 'health__who-gho'), nor does it mention any exclusions or prerequisites for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, so the description does not need to repeat these. It adds valuable context beyond annotations by specifying the data source (disease.sh), update frequency (daily), and the structure of the return value ('Katzilla envelope { data, quality, citation }') with details on quality scores and citation contents. This enhances transparency about data freshness, auditability, and output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details in subsequent sentences. Each sentence adds value: the first states what the tool does, the second specifies source and update frequency, and the third explains the return structure. There is no wasted text, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, source, update frequency, and return format, providing sufficient context for an agent to understand and use the tool effectively without needing to explain parameters or output details that are already documented elsewhere.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the 'days' parameter with type, constraints, and default. The description adds no additional parameter semantics beyond what the schema provides, as it only mentions 'over a number of recent days' without further details. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with a specific verb ('Get'), resource ('global COVID-19 vaccine coverage data'), and scope ('over a number of recent days'). It clearly distinguishes itself from siblings by specifying the data source (disease.sh) and type (vaccine coverage), which is unique among the health-related tools listed (e.g., CDC data, FDA recalls, NIH trials).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('Get global COVID-19 vaccine coverage data over a number of recent days') and mentions the data source and update frequency ('Source: disease.sh (Open Data), updates daily'), which helps guide usage. However, it does not explicitly state when not to use it or name alternatives (e.g., other health data tools for non-vaccine or non-COVID-19 data), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source (openFDA, CC0 license), update frequency (daily), and the return format (Katzilla envelope with data, quality scores, and citation details including SHA-256 hash). This enhances transparency about data freshness, auditability, and output structure, though it doesn't detail rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a structured manner. Every sentence adds value: specifying the return content, data source, update frequency, and output format. It is efficient with no redundant information, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search functionality with two parameters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the description of the Katzilla envelope), the description is complete. It covers purpose, usage context, behavioral traits, and output details, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (limit and query). The description does not add any additional semantic details about parameters beyond what the schema provides, such as query syntax examples or how the limit interacts with pagination. Since the schema is comprehensive, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Search FDA drug adverse event reports (FAERS).' It specifies the resource (FDA adverse event reports), the action (search), and the data source (openFDA). It clearly distinguishes itself from siblings like 'health__fda-devices' or 'health__fda-recalls' by focusing on adverse events rather than devices or recalls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching FDA adverse event reports, including side effects, patient reactions, and drug info. It mentions the source (openFDA) and update frequency (daily), which helps in timing decisions. However, it does not explicitly state when not to use it or name specific alternatives among siblings, though the context implies it's for adverse events specifically.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations: it discloses the data source ('openFDA'), update frequency ('daily'), return format ('Katzilla envelope'), quality metrics ('freshness/uptime/confidence'), and audit features ('SHA-256 data hash'). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format and audit features. Every sentence adds value, with no wasted words, making it front-loaded and highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, 100% schema coverage), rich annotations, and presence of an output schema, the description is complete. It covers purpose, source, update frequency, return format, and audit details, providing all necessary context without needing to explain parameters or return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the 'limit' parameter. The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline of 3 without compensating or enhancing parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('Get') and resource ('recent FDA medical device recall reports'), specifies the data source ('openFDA'), and distinguishes it from sibling tools like 'health__fda-recalls' by focusing on devices rather than general recalls. It clearly defines the tool's scope and differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get recent FDA medical device recall reports'), but does not explicitly mention when not to use it or name specific alternatives among siblings. It implies usage for medical device data, but lacks explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide: it discloses the data source (openFDA), licensing (CC0), update frequency (daily), and the specific return format ('Katzilla envelope { data, quality, citation }') with details about quality scoring and citation contents. While annotations cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, source, and update frequency; the second details the return format and its components. Every element serves a clear purpose with zero wasted words, making it easy to parse while being information-dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, search functionality), rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the existence of an output schema, the description is complete. It covers purpose, data source, update frequency, return format structure, and quality metrics - providing all necessary context for an agent to understand when and how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters ('limit' and 'query'). The description doesn't add any parameter-specific semantics beyond what's in the schema - it mentions 'search query to filter recalls' which merely restates the schema's description for the query parameter. Baseline score of 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search FDA drug enforcement/recall reports') and resources ('FDA drug enforcement/recall reports'), distinguishing it from sibling tools like 'health__fda-adverse-events' or 'health__fda-devices' by focusing specifically on recalls. It also specifies the data source (openFDA) and update frequency (daily).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search FDA drug enforcement/recall reports'), but doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools. It implies usage for recall data needs without contrasting with other FDA or recall-related tools like 'consumer__cpsc-recalls' or 'transport__nhtsa-recalls'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only, non-destructive, idempotent, and open-world operations. The description adds valuable context beyond annotations: it specifies the return format ('Returns the Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 data hash'). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and key details, and the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, high schema coverage, annotations provided, and an output schema), the description is complete. It covers purpose, usage context, behavioral traits, and return format, leaving no gaps. The presence of an output schema means the description does not need to detail return values, and it adequately supplements the structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'din' fully documented. The description adds minimal semantics beyond the schema, only implying the DIN is used for lookup. Since the schema carries the burden, the baseline score of 3 is appropriate, as the description does not significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Look up drugs in Health Canada's Drug Product Database (DPD) by Drug Identification Number (DIN).' It specifies the verb ('look up'), resource ('drugs'), and scope ('Health Canada's Drug Product Database'), distinguishing it from sibling tools that focus on other domains like agriculture, consumer, or economic data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'by Drug Identification Number (DIN)' and mentions the source and update frequency ('Source: Health Canada, updates daily'). However, it does not explicitly state when not to use this tool or name alternatives among siblings, such as other health-related tools like 'health__cdc-data' or 'health__fda-recalls', which serve different purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the return format (Katzilla envelope with data, quality, citation), explains quality scoring (freshness/uptime/confidence), and details citation components (source URL, license, SHA-256 hash). This provides important implementation details not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: first establishes scope and source, second specifies update frequency, third details return format and components. Every sentence adds essential information with zero wasted words, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of comprehensive annotations, 100% schema coverage, and an output schema (implied by the detailed return format description), the description provides complete context. It covers data scope, source, update frequency, return format structure, and quality metrics—everything needed for effective tool use without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (query and limit). The description does not add any parameter-specific semantics beyond what's in the schema, such as query syntax examples or limit usage guidance. The baseline score of 3 is appropriate when the schema carries the full parameter documentation burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving UK health statistics from NHS England across multiple domains (hospital performance, waiting times, etc.). It specifies the data source (NHS England under Open Government Licence) and update frequency (daily), and distinguishes itself from sibling tools by focusing on UK health data rather than other categories like agriculture, crime, or economics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly suggests usage for UK health data queries, but does not explicitly state when to use this tool versus alternatives like health__cdc-data or health__nhs-scotland. It provides context about the data source and update frequency, which helps inform usage decisions, but lacks explicit comparison or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations by specifying the source, update frequency, license, and detailed return structure (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enhances behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and updates; the second details the return format. Every sentence provides essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with structured returns), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context about the data source, license, update frequency, and return format, compensating for any gaps without needing to explain parameters or output details already covered elsewhere.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('query' and 'limit') fully documented in the schema. The description does not add any additional semantic information about parameters beyond what the schema provides, such as query syntax examples or limit usage context. Baseline 3 is appropriate when the schema handles parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with a specific verb ('Search') and resource ('NHS Scotland open data portal for health datasets'), and distinguishes it from siblings by specifying the source (NHS Scotland Open Data) and return format (Katzilla envelope). This clearly differentiates it from other health tools like 'health__cdc-data' or 'health__nhs-england'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying the data source (NHS Scotland Open Data), update frequency (daily), and license (UK Open Government License). However, it does not explicitly state when to use this tool versus alternatives like 'health__nhs-england' or other health data tools, nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (ClinicalTrials.gov), update frequency (daily), return format (Katzilla envelope with quality scores and citation details), and that it's public domain. While annotations cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details that help the agent understand data provenance and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality, and the second provides critical metadata about source, updates, and return format. Every phrase adds value (e.g., 'Public Domain', 'SHA-256 data hash for audit') without redundancy, making it front-loaded and zero-waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, search functionality), the description is complete: it covers purpose, source, update frequency, return format, and data quality aspects. With annotations providing safety hints and an output schema presumably detailing the Katzilla envelope structure, no essential information is missing for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters ('query' and 'limit'). The description mentions 'keyword' which aligns with 'query' but doesn't add any meaningful semantic details beyond what the schema provides (e.g., search syntax examples, typical query patterns). This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with specific verbs ('Search ClinicalTrials.gov for clinical studies by keyword') and resources ('clinical studies'), and distinguishes it from siblings by specifying the exact data source (ClinicalTrials.gov) and return format (Katzilla envelope). This is more specific than just 'search clinical trials' and clearly differentiates it from other health tools like 'nih-reporter' or 'pubmed'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search ClinicalTrials.gov for clinical studies by keyword') and implies it's for finding studies by keyword rather than other criteria. However, it doesn't explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., 'nih-reporter' or 'pubmed'), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (CMS NPPES), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation including SHA-256 hash). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description appropriately supplements with practical usage information without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence, with no wasted words. Each sentence provides essential information (search functionality, data source, return format), making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple parameters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete enough. It covers the purpose, data source, update frequency, and return format, which, combined with structured fields, provides a comprehensive understanding without needing to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions search parameters ('by NPI number, first name, or last name'), which aligns with the input schema properties. However, with 100% schema description coverage, the schema already fully documents all four parameters, so the description does not add significant semantic value beyond what is in the schema. It meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search the CMS NPPES NPI Registry') and resources ('healthcare providers'), distinguishing it from sibling tools by specifying the exact data source (CMS NPPES NPI Registry) and search parameters (NPI number, first name, last name). It avoids tautology by providing operational details beyond the name/title.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('Search... for healthcare providers by NPI number, first name, or last name') and mentions the data source and update frequency, which helps in decision-making. However, it does not explicitly state when not to use it or name alternative tools for similar purposes, such as other health-related sibling tools like 'health__cdc-data' or 'health__nih-clinical-trials'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it discloses the data source ('NCBI PubChem (Public Domain)'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality scoring and citation components. This enriches the agent's understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, data fields, and search methods; the second covers source, updates, and return format. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, read-only operation), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the tool's scope, data source, update frequency, and return structure, leaving no significant gaps for the agent to understand its use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'name' fully documented in the schema. The description mentions 'Search by common name or CID', which adds slight semantic context about acceptable input types, but does not provide additional details beyond what the schema already states (e.g., examples like 'aspirin' are already in the schema). This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('look up', 'search') and resources ('chemical compound data from NCBI PubChem'), listing exact data fields returned (molecular formula, weight, IUPAC name, etc.). It distinguishes itself from sibling tools by focusing specifically on PubChem compound data, unlike other health tools that cover different data sources like CDC, FDA, or clinical trials.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Look up chemical compound data from NCBI PubChem') and specifies search methods ('Search by common name or CID'), but does not explicitly state when not to use it or name specific alternative tools for similar queries. The context is sufficient for basic usage decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, but the description adds valuable context beyond this. It specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality scores ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'), which are not covered by annotations. No contradiction with annotations is present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured and front-loaded, starting with the core purpose, followed by source details and return format explanation. Every sentence adds value without redundancy, making it easy to parse quickly while providing comprehensive information in a compact form.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (drug lookup with quality metrics), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, return structure, and quality aspects, leaving no significant gaps for an AI agent to understand and invoke the tool effectively without needing to rely solely on structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'name' well-documented in the schema. The description does not add any additional meaning or examples beyond what the schema provides (e.g., it doesn't clarify search behavior like partial matches or case sensitivity). Given the high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate with extra parameter insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('look up drug information') and resources ('from NLM RxNorm'), listing exact information types (brand names, generic names, dosage forms, RxCUI identifiers). It distinguishes itself from sibling tools by specifying its domain (health/medical drug data) and source (NLM RxNorm), unlike other health tools like CDC data or clinical trials.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('look up drug information from NLM RxNorm') and mentions the source and update frequency, which helps in understanding its scope. However, it does not explicitly state when not to use it or name specific alternatives among sibling tools, such as other health data sources like PubChem or FDA data, leaving some ambiguity in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source (USDA FoodData Central, Public Domain), update frequency (daily), and details about the return format (Katzilla envelope with data, quality scores, and citation including SHA-256 hash). This enhances transparency about data freshness, auditability, and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format and its components. Every sentence adds essential information (e.g., update frequency, output envelope structure), with no redundant or vague phrasing. It is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers purpose, source, update frequency, and output details, providing sufficient context for an agent to use the tool effectively without needing to explain return values redundantly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the 'query' and 'limit' parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't clarify query syntax or result ordering). With high schema coverage, the baseline is 3, as the description relies on the schema for parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Search the USDA FoodData Central database for nutrition information on foods.' It specifies the verb ('Search'), resource ('USDA FoodData Central database'), and what it returns ('nutrition information on foods'). It distinguishes from siblings by focusing on nutrition data, unlike other USDA tools (e.g., agriculture__usda-nass for agricultural statistics).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching nutrition information from the USDA FoodData Central database. It mentions the source updates daily, implying it's suitable for current data needs. However, it does not explicitly state when not to use it or name specific alternatives among the many sibling tools, though the health domain context helps differentiate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the bar is lower. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality scoring ('freshness/uptime/confidence'), and details citation contents ('source URL, license, SHA-256 data hash'). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, and source; the second details the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, compensating well for any gaps and aligning with the structured data provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (city, state, year) with descriptions and defaults. The description adds no additional parameter semantics beyond implying they are used for filtering rates by location and year, which is already clear from the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get federal per diem rates') and resources ('from the U.S. General Services Administration'), including scope details ('lodging and meals & incidentals (M&IE) rates by city and state'). It distinguishes itself from siblings by focusing on GSA per diem data, unlike other housing tools (e.g., HUD-related) or unrelated categories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get federal per diem rates... by city and state') and mentions the source and update frequency ('updates monthly'), which helps in timing decisions. However, it does not explicitly state when not to use it or name specific alternatives among siblings, though the context implies it's for GSA-specific rates.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world hints, but the description specifies the data source (U.S. Department of Housing and Urban Development), update frequency (quarterly), and the return format (Katzilla envelope with data, quality scores, and citation details including a SHA-256 hash). This enhances transparency about data freshness, auditability, and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured. It front-loads the core purpose, specifies geographic levels and data source, then details the return format and quality metrics in a single, efficient paragraph. Every sentence adds value without redundancy, making it easy for an agent to quickly grasp the tool's function and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers the purpose, data source, update frequency, and return format, which complements the structured fields. The output schema likely details the Katzilla envelope, so the description need not explain return values further, making it adequately comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already documents all parameters thoroughly. The description adds minimal parameter semantics, only implying geographic levels and year ranges without providing additional syntax or format details. It meets the baseline of 3 since the schema carries the full burden, but does not compensate with extra insights like examples or constraints beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query the HUD Comprehensive Housing Affordability Strategy (CHAS) data. Provides housing affordability metrics at nation, state, or county level.' It specifies the exact resource (CHAS data) and the geographic levels, distinguishing it from sibling tools like 'housing__hud-fmr' or 'housing__hud-income-limits' by focusing on affordability metrics rather than fair market rents or income limits.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for querying housing affordability metrics at specific geographic levels (nation, state, county) from a U.S. government source with quarterly updates. However, it does not explicitly state when to use this tool versus alternatives like 'demographics__census-acs' or other housing-related tools, nor does it mention prerequisites or exclusions beyond the geographic scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation contents ('source URL, license, SHA-256 hash'). This enriches the agent's understanding of output behavior and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context and output details. Every sentence adds value: explaining FMR usage, data source, update frequency, and return structure. It is efficiently structured without redundancy or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, 100% schema coverage, annotations, and output schema), the description is complete. It covers purpose, usage context, behavioral traits beyond annotations, and output format. With an output schema present, the description appropriately focuses on high-level return structure without needing to detail all return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed descriptions for each parameter (e.g., 'U.S. state/territory code', 'HUD entity ID', 'FMR fiscal year'). The description does not add further parameter semantics beyond the schema, such as explaining the relationship between stateCode and entityId in more depth. Baseline 3 is appropriate as the schema adequately documents parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Get Fair Market Rent (FMR) data from the U.S. Department of Housing and Urban Development (HUD).' It specifies the verb ('Get'), resource ('FMR data'), and source ('HUD'), and distinguishes it from siblings by focusing on rental voucher payment amounts for metro areas, unlike other housing tools like 'hud-chas' or 'hud-income-limits'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: to retrieve FMR data for determining rental voucher payments. It mentions the data source and update frequency ('updates quarterly'), which helps in timing usage. However, it does not explicitly state when not to use it or name alternatives among siblings, such as for income limits or housing characteristics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (U.S. Department of Housing and Urban Development), update frequency (quarterly), and return format (Katzilla envelope with quality scores and citation details). Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, so the description complements this by explaining data freshness and auditability without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with two sentences that efficiently cover purpose, context, source, updates, and return format. Every sentence adds essential information without redundancy, making it easy to scan and understand the tool's key aspects.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, usage context, behavioral traits, and output details, leaving no significant gaps for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents the three parameters (stateCode, entityId, year) with descriptions, enums, defaults, and constraints. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining the relationship between stateCode and entityId in more detail, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get'), resource ('income limit data from the U.S. Department of Housing and Urban Development'), and context ('determine eligibility for HUD assisted housing programs by metro area'). It distinguishes itself from sibling tools by focusing on HUD income limits, which is unique among the listed siblings that cover various domains like agriculture, consumer, crime, etc., with no other HUD-specific tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to retrieve income limit data for HUD programs, with source and update frequency mentioned. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as other housing tools like 'housing__hud-chas' or 'housing__hud-fmr', which might serve different purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source (HM Land Registry), update frequency (monthly), licensing (Open Government Licence), and details about the return format (Katzilla envelope with quality scores and citation data including SHA-256 hash). This significantly enhances understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, and data fields; the second covers source, updates, and return format. Every element serves a clear purpose with zero wasted words, and critical information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, 100% schema coverage, comprehensive annotations, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, scope, data source, update frequency, licensing, and return format details—everything needed to understand the tool's value and limitations without duplicating structured field information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 6 parameters. The description mentions the three location parameters (postcode, town, county) and implies price filtering, but doesn't add meaningful semantic context beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search property transactions by postcode, town, or county' with specific data fields (sale price, property type, tenure, transaction date) and geographical scope (England and Wales). It distinguishes itself from siblings by focusing on UK housing data, unlike other tools in the list that cover different domains like agriculture, crime, or crypto.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for UK house price data from HM Land Registry, with monthly updates. It doesn't explicitly mention when not to use it or name specific alternatives among siblings, but the domain-specific context (housing vs. other categories) strongly implies appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source ('4chan (Public API)'), update frequency ('updates monthly'), and detailed return format ('Katzilla envelope with quality scores and citation details'). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description effectively supplements them without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured in two sentences: the first states the purpose and parameter logic, and the second details the return format and source. Every sentence adds essential information without redundancy, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations covering safety and behavior, and the presence of an output schema, the description is complete. It effectively explains the tool's functionality, data source, update cadence, and return structure, leaving no significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents the single parameter. The description adds minimal semantic value by mentioning the parameter's effect (board list vs. catalog) but does not provide additional syntax or format details beyond what the schema states, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get 4chan board list or catalog for a specific board'), identifies the resource ('4chan'), and distinguishes it from siblings by specifying its unique data source and return format. It explicitly mentions what happens when the parameter is omitted versus provided.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context by explaining that omitting the 'board' parameter returns a board list, while providing it returns a catalog for that board. However, it does not explicitly mention when to use this tool versus alternatives or any prerequisites, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it discloses the data source ('Agify'), update frequency ('monthly'), return format ('Katzilla envelope'), and details about quality scores and citation data. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and source, the second details the return format and its components. Every phrase adds value (e.g., 'Free Tier', 'updates monthly', quality/citation breakdown), with no redundant or vague language.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, 100% schema coverage, annotations provided, output schema exists), the description is complete. It covers purpose, source, behavioral context, and output structure, leaving no gaps for the agent to infer. The existence of an output schema means the description need not explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'name' parameter clearly documented. The description does not add any additional semantic details about the parameter beyond what the schema provides (e.g., no examples, formatting rules, or constraints). Baseline 3 is appropriate since the schema fully covers the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Predict the age of a person based on their name.' It specifies the verb ('predict'), resource ('age'), and distinguishes it from siblings by mentioning the data source 'Agify (Free Tier)' and the unique 'Katzilla envelope' return format, which no other sibling tool references.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for age prediction based on names using the Agify service. It mentions the 'Free Tier' and 'updates monthly' to set expectations, but does not explicitly state when not to use it or name alternative tools for similar demographic data (e.g., other international tools like 'genderize' or 'nationalize').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the source (APIs.guru), update frequency (monthly), and details about the return structure (Katzilla envelope with quality scores and citation info), which helps the agent understand data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every sentence adds value without redundancy, making it front-loaded and appropriately sized for a zero-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple read operation with no parameters), rich annotations (covering safety and idempotency), and the presence of an output schema, the description is complete. It adds necessary context about the source, update frequency, and return structure, ensuring the agent understands the data's nature and limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on output semantics. It adds meaning by explaining the return format ('Katzilla envelope') and its components, which compensates for the lack of input parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get a summary of all public APIs tracked by APIs.guru') and resource ('public APIs'), distinguishing it from siblings by focusing on API metadata rather than specific data domains like agriculture, crime, or economics. It explicitly mentions the source and update frequency, which adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving API summaries from APIs.guru, but does not explicitly state when to use this tool versus alternatives among the many sibling tools. It mentions the source and data characteristics, providing some context, but lacks direct guidance on alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source (Binlist Free API), update frequency (monthly), and details about the return format (Katzilla envelope with quality scores and citation). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, high schema coverage, annotations provided, and an output schema exists), the description is complete. It covers purpose, source, update frequency, and return format, which is sufficient for an agent to understand and use the tool effectively without needing to explain return values (handled by output schema).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'bin' parameter clearly documented as 'Bank Identification Number (first 6-8 digits of card)'. The description does not add any additional semantic details about the parameter beyond what the schema provides, such as validation rules or examples. Thus, it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Look up card BIN/IIN information (bank, type, brand, country)' with a specific verb ('look up') and resource ('card BIN/IIN information'). It distinguishes itself from siblings by specifying its unique data source (Binlist) and return format (Katzilla envelope), making it highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for looking up card BIN/IIN information. It mentions the source (Binlist Free API) and update frequency (monthly), which helps set expectations. However, it does not explicitly state when not to use it or name alternative tools for similar purposes, such as other financial lookup tools that might exist among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world hints, but the description elaborates on the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'). This enriches the agent's understanding of the tool's behavior and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured, consisting of two sentences that efficiently convey purpose, source, and return format. Every sentence adds critical information without redundancy, making it easy for the agent to parse and understand the tool's functionality quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search operation with structured output), rich annotations (read-only, etc.), and the presence of an output schema, the description is complete. It explains the purpose, source, and return format in detail, compensating for any gaps and ensuring the agent has sufficient context to use the tool effectively without needing to infer missing information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents the two parameters ('query' for library name and 'limit' for max results). The description does not add any additional semantic details about these parameters, such as examples or constraints beyond the schema. Thus, it meets the baseline of 3 by not detracting from the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search JavaScript libraries on cdnjs CDN.' It specifies the verb ('Search'), resource ('JavaScript libraries'), and platform ('cdnjs CDN'), making it distinct from sibling tools that focus on other domains like agriculture, crime, or economics. This precise articulation helps the agent understand exactly what this tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching JavaScript libraries on cdnjs. It mentions the source ('cdnjs (Free API), updates monthly'), which implies it's suitable for up-to-date but not real-time data. However, it does not explicitly state when not to use it or name alternatives among siblings, such as other library search tools, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond this: it discloses the data source (CityBikes Free API), update frequency (monthly), and detailed return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format and its components. Every element (e.g., data source, update frequency, envelope structure) serves a clear informational purpose without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, rich annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return format, which, combined with structured fields, provides all necessary context for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'limit' parameter fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get bike-sharing networks worldwide'), identifies the resource ('from the CityBikes API'), and distinguishes it from siblings by specifying the data source and return format. It explicitly mentions the 'International Data Agent' context, which differentiates it from other tools in the list that focus on different domains like agriculture, consumer, crime, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for accessing bike-sharing network data from a specific API (CityBikes) with monthly updates. However, it does not explicitly state when not to use it or name alternative tools for similar data, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the agent knows it's a safe, cacheable query. The description adds valuable context beyond annotations: 'updates monthly' (frequency), 'Returns the Katzilla envelope { data, quality, citation }' (output structure), and details on quality scoring and citation components (source URL, license, SHA-256 hash). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source, update frequency, and output details in a compact two-sentence format. Every sentence adds value: the first defines the tool, and the second explains the return structure and audit features, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations (readOnlyHint, idempotentHint, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update cadence, and output semantics, providing sufficient context for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single optional 'date' parameter, documented as 'Date in YYYY-MM-DD format (optional).' The description doesn't add further parameter details beyond implying date-based queries ('daily exchange rates'), so it meets the baseline of 3 where the schema handles documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get daily exchange rates from the Czech National Bank.' It specifies the verb ('Get'), resource ('daily exchange rates'), and source ('Czech National Bank'), distinguishing it from sibling tools like 'economic__exchange-rates' or 'economic__ecb-rates' by identifying the specific central bank source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'updates monthly' and 'daily exchange rates,' indicating it's for historical or recent data rather than real-time updates. It doesn't explicitly state when not to use it or name alternatives, but the source specificity implies it's for Czech koruna rates from CNB.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the data source (Fruityvice Free API), update frequency (monthly), and details about the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). This enhances transparency about data freshness, reliability, and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a structured manner. It efficiently conveys source, update frequency, and return format without unnecessary words, making every sentence contribute essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple read operation with one optional parameter), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context about data source, update frequency, and return structure, compensating for any gaps and ensuring the agent has sufficient information to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'name' documented as 'Fruit name (omit for all fruits)'. The description does not add further details about parameter usage or semantics beyond what the schema provides. With high schema coverage, the baseline score of 3 is appropriate, as the description relies on the schema for parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Get nutritional data about fruits') and the resource ('from Fruityvice'), making the purpose clear. It distinguishes itself from sibling tools by specifying the data domain (fruits/nutrition) and source (Fruityvice API), unlike other tools in the list that cover agriculture, consumer data, crypto, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for obtaining nutritional data about fruits from the Fruityvice API. It mentions the source and update frequency (monthly), which helps set expectations. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as agriculture__usda-fooddata for broader food data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, covering safety and behavior. The description adds valuable context beyond annotations: it specifies the data source, update frequency ('updates monthly'), and details about the return structure ('quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit'), which aids in understanding reliability and auditability without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured in two sentences: the first states the purpose and source, and the second explains the return format and quality metrics. Every sentence adds essential information without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, 100% schema coverage), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return structure, providing all necessary context for an AI agent to use the tool effectively without needing to explain return values, as the output schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'name' documented as 'Name to predict gender for'. The description does not add further semantic details about the parameter, such as format examples or constraints, but since the schema already provides adequate information, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Predict the gender of a person based on their name.' It specifies the verb ('predict'), resource ('gender'), and input ('name'), and distinguishes it from siblings by mentioning the data source 'Genderize (Free Tier)' and the specific return format 'Katzilla envelope', which is unique among the listed tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by stating the source ('Genderize (Free Tier)') and update frequency ('updates monthly'), which helps in deciding when to use it. However, it does not explicitly mention when not to use it or name alternatives, such as other demographic tools like 'demographics__census-acs' or 'demographics__rest-countries', which could offer gender data in different contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description adds valuable context beyond this: it discloses the data source ('GOV.UK (Open Government Licence)'), update frequency ('updates monthly'), and output structure ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation components. This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and output explanation in two efficient sentences. Every sentence adds value: the first defines the action, the second clarifies output structure and audit features. No wasted words, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with quality metrics), rich annotations (readOnlyHint, idempotentHint, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and output format, leaving no gaps for the agent to understand tool behavior and context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('query' and 'limit') fully described in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or limit constraints. Baseline score of 3 is appropriate as the schema handles parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('Search'), resource ('UK government website GOV.UK for content'), and scope ('International Data Agent'), distinguishing it from siblings like 'government__data-uk' or 'government__govinfo-search' by specifying the source and international context. It's specific and avoids tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Search the UK government website GOV.UK for content') and mentions the source and update frequency, but does not explicitly state when not to use it or name alternatives among the many sibling tools, such as 'government__data-uk' or 'media__gnews'. It implies usage for UK government content searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, covering safety and reliability. The description adds valuable context beyond this by disclosing the source ('Hebcal (Free API)'), update frequency ('updates monthly'), and the return format ('Katzilla envelope with quality scores and citation details'), enhancing transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential details about the source, updates, and return format in a compact manner. Every sentence contributes necessary information without redundancy, making it efficiently structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 optional parameters, 100% schema coverage, annotations covering key behavioral traits, and an output schema implied by the return format description), the description is complete. It adequately explains the tool's function, source, update cadence, and output structure, leaving no significant gaps for an AI agent to understand and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for 'year' and 'month' parameters. The description does not add any additional meaning or syntax details beyond what the schema provides, such as how these parameters affect the output. Thus, it meets the baseline for high schema coverage without compensating further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with a specific verb ('Get') and resource ('Jewish calendar events and holidays from Hebcal'), clearly distinguishing it from sibling tools that cover diverse domains like agriculture, consumer data, or entertainment. It directly addresses what the tool does without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool by specifying the data source ('Hebcal (Free API)') and update frequency ('updates monthly'), which helps in decision-making. However, it does not explicitly state when not to use it or name alternatives among sibling tools, which are all in different categories, so differentiation is implied but not direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source (Hipolabs Free API), update frequency (monthly), and detailed return format (Katzilla envelope with quality scores and citation details). Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description complements them without contradiction, enhancing transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by essential operational details (source, updates, return format). Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with filtering), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits, and return format, leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear parameter descriptions ('Country name', 'University name'). The description adds no additional parameter semantics beyond implying filtering by country/name, which is already evident from the schema. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search'), resource ('universities worldwide'), and scope ('by country and/or name'). It distinguishes itself from sibling tools like 'education__hipolabs-universities' by specifying the international focus and data source (Hipolabs API), avoiding redundancy.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Search universities worldwide by country and/or name') and mentions the data source and update frequency, which helps set expectations. However, it does not explicitly state when not to use this tool or name specific alternatives among the many sibling tools, which would be needed for a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable context beyond this: it specifies the return format ('Katzilla envelope'), explains quality scoring ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'), which are not captured in annotations. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, full schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return format details, which, combined with structured data, provides all necessary context for an agent to use the tool effectively without needing additional explanation of return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter ('limit') fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, such as range constraints or usage examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get top Hacker News story IDs'), resource ('via the Firebase API'), and source ('Hacker News (Public API)'). It distinguishes itself from siblings like 'media__hacker-news' by specifying the Firebase API method and monthly update cadence, making the purpose explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning the source (Hacker News Public API) and update frequency (monthly), which helps determine when to use it. However, it does not explicitly state when not to use it or name alternatives (e.g., other Hacker News tools), so it lacks full comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), covering safety and idempotency. The description adds valuable context beyond this: it discloses the source ('Nationalize (Free Tier)'), update frequency ('updates monthly'), and return format ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation contents. This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details in a structured manner. Every sentence adds value: the second sentence specifies the source and update frequency, and the third explains the return format and its components. There is no wasted text, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter), rich annotations (covering safety and behavior), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, providing sufficient context for an agent to use the tool effectively without needing to explain basic parameters or output details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'name' parameter fully documented. The description does not add any additional semantic details about the parameter beyond what the schema provides (e.g., format examples or constraints). Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Predict the nationality of a person based on their name.' It specifies the verb ('predict'), resource ('nationality'), and scope ('based on their name'), distinguishing it from sibling tools like 'agify' or 'genderize' which predict different attributes. This is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Source: Nationalize (Free Tier), updates monthly.' This indicates when to use it (for nationality prediction via this specific API) and implies limitations (free tier, monthly updates). However, it does not explicitly state when not to use it or name alternatives among siblings, such as other demographic tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the return format ('Katzilla envelope { data, quality, citation }'), explains what quality scores measure ('freshness/uptime/confidence'), and describes citation components ('source URL, license, and a SHA-256 data hash'). This complements the annotations which already indicate read-only, non-destructive, idempotent, and open-world characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and source, the second explains the return format and its components. Every element serves a clear purpose with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simple single parameter, comprehensive annotations, and existence of an output schema, the description provides complete context. It explains what the tool does, its data source, update frequency, and return format structure, making it fully adequate for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage and only one parameter with a clear enum and default value, the schema already fully documents the parameter. The description doesn't add any additional parameter semantics beyond what's in the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get exchange rates'), resource ('from the National Bank of Poland'), and scope ('Source: NBP (Public Data), updates monthly'). It distinguishes itself from siblings like 'economic__exchange-rates' by specifying the Polish national bank source and monthly update cadence.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Get exchange rates from the National Bank of Poland') and mentions the data source and update frequency. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for different exchange rate sources.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source (TheSportsDB), usage restrictions ('Free for non-commercial use'), update frequency ('updates monthly'), and output format details ('Returns the Katzilla envelope { data, quality, citation }' with quality scores and citation info). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, filtering, and return details; the second adds source, usage terms, and output format specifics. Every sentence contributes essential information without redundancy, making it front-loaded and concise for quick agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits, source details, and output structure, providing all necessary information for an agent to invoke the tool correctly without needing to explain return values explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('date' and 'sport'). The description adds minimal semantic value beyond the schema by mentioning filtering by 'date and sport' but does not provide additional details like example sport values or date range constraints. Given the high schema coverage, a baseline score of 3 is appropriate as the schema handles most parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Upcoming sports events from TheSportsDB. Filter by date and sport. Returns event details including teams, league, venue, and schedule.' It specifies the verb ('returns'), resource ('upcoming sports events'), and distinguishes from siblings by focusing on TheSportsDB source and filtering capabilities, unlike other sports tools like 'sports__league-schedules' or 'sports__live-scores'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Filter by date and sport' and notes the source is 'Free for non-commercial use, updates monthly.' However, it does not explicitly state when not to use this tool or name specific alternatives among siblings, such as 'sports__openligadb' for different data sources, leaving some ambiguity in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the API source (Frankfurter), update frequency (monthly), and details the return format (Katzilla envelope with quality scores and citation). This enhances behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured into two sentences: the first states the purpose and usage, and the second details the source, update frequency, and return format. Every sentence adds essential information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (currency exchange rates), rich annotations (read-only, idempotent, open-world), 100% schema coverage, and the presence of an output schema, the description is complete. It covers the purpose, usage, source, update frequency, and return format, providing all necessary context for an AI agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('base' and 'symbols') well-documented in the schema. The description mentions 'optional symbol filtering' and 'any base currency,' which aligns with but does not add significant meaning beyond the schema. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Latest currency exchange rates via Frankfurter API. Get rates for any base currency with optional symbol filtering.' It specifies the verb ('get'), resource ('currency exchange rates'), and distinguishes it from sibling tools like 'economic__exchange-rates' by mentioning the specific API source (Frankfurter) and the Katzilla envelope return format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Get rates for any base currency with optional symbol filtering' and notes the source and update frequency ('Source: Frankfurter (European Central Bank) (Free / Open), updates monthly'). However, it does not explicitly state when to use this tool versus alternatives like 'economic__exchange-rates' or other sibling tools, which prevents a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description adds valuable context: it discloses the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'), which are not inferable from annotations alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format explanation in a compact two-sentence structure. Every sentence adds essential information without redundancy, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adequately explains the tool's purpose, usage context, and output without needing to detail parameters or return values further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('league', 'season') well-documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, so it meets the baseline for high schema coverage without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Get football/soccer match data'), resource ('from OpenLigaDB'), and scope ('German leagues focus'), which is specific and distinguishes it from sibling tools like 'sports__live-scores' or 'sports__team-info' that might handle different sports data or formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear context for when to use this tool (for football/soccer match data from OpenLigaDB with a German focus) and mentions the source and update frequency ('updates monthly'), but does not explicitly state when not to use it or name alternative tools for similar data, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the data source ('Radio Browser (Free API)'), update frequency ('updates monthly'), and details the return envelope structure ('Katzilla envelope { data, quality, citation }') with quality metrics and citation components. This enriches understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and updates; the second details the return format. Every sentence adds essential information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the purpose, data source, update cadence, and return structure, providing sufficient context for effective use without needing to detail output values further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('country' and 'limit'). The description mentions filtering by country, aligning with the schema, but does not add significant semantic details beyond what the schema already provides. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search internet radio stations worldwide by country'), resource ('radio stations'), and scope ('worldwide by country'). It distinguishes from sibling tools like 'fun__radio-browser' by emphasizing international data and monthly updates, making the purpose explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search internet radio stations worldwide by country'), but does not explicitly mention when not to use it or name specific alternatives among siblings. The context is sufficient for typical usage, though lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source (World Bank Development Indicators API), update frequency (monthly), licensing (CC BY 4.0), and detailed return structure (Katzilla envelope with quality scores and citation details). While annotations already cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details like data lag (~2 years) and audit features (SHA-256 hash).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, parameters, and data source; the second details return format, quality metrics, and citation. Every element adds value without redundancy, and it is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), 100% schema coverage, and the presence of an output schema (implied by return format description), the description is complete. It covers purpose, usage context, behavioral traits, and output structure, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all three parameters (country, year, indicator) with defaults and descriptions. The description adds marginal value by mentioning the data lag for year and listing example indicator IDs, but does not provide significant additional semantics beyond what the schema offers, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query World Bank trade indicators') and resources ('exports, imports, trade balance'), and distinguishes it from siblings by specifying the exact data source (World Bank Development Indicators API). It explicitly mentions the return format (Katzilla envelope), which helps differentiate it from other economic/trade tools in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('by country and year'), mentions the data source and update frequency, and implies usage for trade indicator queries. However, it does not explicitly state when not to use it or name alternative tools (e.g., economic__world-bank, trade__eurostat-trade from the sibling list), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it discloses the real-time update nature, the source (Digitraffic with Creative Commons license), and details about the return structure (Katzilla envelope with quality scores and citation data including a SHA-256 hash for audit). This enriches the agent's understanding of data freshness, licensing, and output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and usage, and the second details the return format and source information. Every sentence adds critical value—no wasted words—and it's front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (real-time data lookup with quality metrics), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, licensing, update frequency, and return structure, providing all necessary context for an agent to use the tool effectively without needing to explain return values (handled by the output schema).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the parameters (mmsi, shipName, limit). The description mentions looking up by MMSI or name, which aligns with the schema but doesn't add significant semantic details beyond what's already in the schema descriptions. It does not explain parameter interactions or constraints, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Vessel metadata and registration data from Digitraffic maritime AIS. Look up vessels by MMSI or name.' It specifies the verb ('look up'), resource ('vessel metadata and registration data'), and source ('Digitraffic maritime AIS'), clearly distinguishing it from sibling tools that cover different domains like agriculture, consumer, or crypto.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: for real-time maritime vessel lookups by MMSI or name, with a specified data source. It does not explicitly mention when not to use it or name alternative tools for similar purposes, but the domain-specific context (maritime vs. other sibling domains) offers implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is read-only, non-destructive, idempotent, and open-world. The description adds valuable behavioral context beyond annotations: it specifies the source ('GNews (GNews Terms)'), update frequency ('updates hourly'), and details the return format ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation components. This enriches understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return format, compensating for any gaps. With annotations and output schema handling safety and structure, no additional details are necessary for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear defaults and descriptions for all parameters (category, lang, limit). The description does not add significant semantic details beyond the schema, as it only mentions 'by category and language' without elaborating on parameter usage or constraints. Given the high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get top news headlines') and resources ('from GNews'), specifying the filtering criteria ('by category and language'). It distinguishes itself from sibling tools by focusing on news headlines from a specific source (GNews), unlike other media tools like 'media__hacker-news' which targets a different platform.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Get top news headlines by category and language from GNews') and mentions the update frequency ('updates hourly'), which helps determine when to use it. However, it does not explicitly state when not to use this tool or name specific alternatives among siblings, such as when needing different news sources or more detailed articles.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable context beyond this: it specifies the source ('Hacker News (Public API)'), update frequency ('real-time'), and details about the return format ('Katzilla envelope' with quality scores and citation info including SHA-256 hash). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and data returned, and the second adds source, update info, and return format details. Every sentence contributes essential information without waste, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the description of the return format), the description is complete. It adequately explains what the tool does, its source, update behavior, and output structure, leaving no significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'limit' fully documented in the schema. The description does not add any additional meaning or details about parameters beyond what the schema provides, so it meets the baseline for high schema coverage without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get the top stories') and resources ('from Hacker News'), including what data it returns ('titles, URLs, and scores'). It distinguishes itself from siblings by specifying it's for Hacker News content, unlike other media or unrelated tools in the list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get the top stories from Hacker News') and implies it's for real-time updates, but does not explicitly state when not to use it or name alternatives among siblings. The context is sufficient for typical usage without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it discloses the data source ('Data.gov (Public Domain)'), update frequency ('updates daily'), and details about the return structure ('quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit'), enhancing transparency without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first ('Search the US Data.gov open data catalog for datasets'), followed by essential details (source, update frequency, return format). Every sentence adds value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search functionality with structured returns), rich annotations (covering read-only, non-destructive, idempotent, open-world), 100% schema coverage, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It explains the purpose, source, update frequency, and return structure, compensating adequately without needing to detail parameters or output values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('query' as 'Search query' and 'limit' as 'Max results to return'). The description does not add any additional meaning or syntax details beyond what the schema provides, such as query format examples or limit constraints, so it meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the US Data.gov open data catalog for datasets'), identifies the resource ('datasets'), and distinguishes from siblings by specifying the source ('Data.gov') and the structured return format ('Katzilla envelope { data, quality, citation }'), which is unique among the listed tools that focus on various domains like agriculture, consumer, crypto, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Search the US Data.gov open data catalog for datasets') and implies when to use it (for open data catalog searches), but does not explicitly state when not to use it or name specific alternatives among the siblings, such as other government data tools like 'government__datagov' or 'government__datagov-dataset'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (World Bank, SIPRI indicator), license (CC BY-4.0), update frequency (annual), and return format (Katzilla envelope with quality scores and citation details). This enhances transparency about data provenance and output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and scope, and the second provides essential metadata about data source, updates, and return format. Every sentence adds value without redundancy, making it front-loaded and concise for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (querying a specialized database), rich annotations (covering safety and idempotency), 100% schema coverage, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context about data sourcing, licensing, and output structure, compensating adequately where structured fields might not fully convey operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed descriptions for 'country' (ISO 3166-1 alpha-3 code or 'all'), 'from' (start year), and 'to' (end year with lag note). The description adds minimal parameter semantics beyond the schema, only implying year-range filtering. Given the high schema coverage, a baseline score of 3 is appropriate as the schema carries most of the parameter documentation burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query SIPRI Military Expenditure Database for global defense spending data by country and year range.' It specifies the exact resource (SIPRI database), verb (query), and scope (global defense spending), distinguishing it from sibling tools like military__sipri-companies and military__sipri-transfers which handle different military data aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Annual data available from 1960 to present' and 'SIPRI data lags ~2 years' in the schema. However, it does not explicitly state when to use this tool versus alternatives like economic__world-bank or other military tools, nor does it mention exclusions or prerequisites. The guidance is helpful but not comprehensive for sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it discloses data source details (SIPRI via World Bank, CC BY-4.0 license), update frequency (annual), data lag (~2 years for end year), and output structure (Katzilla envelope with quality scores and citation including SHA-256 hash). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and parameters, the second covers data source, updates, and output format. Every sentence adds critical information (e.g., output envelope details, licensing, audit features) with zero wasted words, making it highly front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by the description of the Katzilla envelope), the description is complete. It adequately explains the tool's function, data provenance, behavioral traits, and output structure without needing to detail return values explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for all parameters (country as ISO 3166-1 alpha-3 code, from/to as years with ranges and defaults). The description adds minimal semantic value beyond the schema, only implying year-range filtering without additional syntax or format details. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: querying 'country-level military expenditure data from the SIPRI Military Expenditure Database' with specific parameters (country and year range) and output details (current USD and % of GDP). It clearly distinguishes from siblings like 'military__sipri-companies' and 'military__sipri-expenditure' by focusing on transfers/expenditure data rather than company information or other military metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for retrieving military expenditure data by country and year range, sourced via World Bank from SIPRI. It implies usage for data analysis or reporting needs. However, it does not explicitly state when not to use it or name specific alternatives among siblings, though the distinction is inferable from tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the source ('arXiv'), terms of use, update frequency ('updates daily'), and details about the return structure ('Katzilla envelope { data, quality, citation }') including quality scores and citation metadata. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and return fields, and the second adds source, update frequency, and return structure details. Every sentence provides essential information without redundancy, making it front-loaded and zero-waste, which is ideal for quick agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with two parameters), rich annotations (covering read-only, non-destructive, idempotent, open-world traits), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, source, update behavior, and return structure, compensating well for any gaps. With annotations and output schema handling safety and output details, no additional explanation is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('query' and 'limit') well-documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain query syntax or limit implications). Given the high schema coverage, the baseline score of 3 is appropriate, as the description adds no extra parameter details but doesn't need to compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the arXiv preprint server for scientific papers') and resource ('arXiv preprint server'), distinguishing it from siblings like 'science__pubmed' or 'science__semantic-scholar' by specifying the arXiv source. It explicitly mentions the return fields (titles, authors, abstracts, arXiv IDs) and the Katzilla envelope structure, making the purpose highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Search the arXiv preprint server for scientific papers') and mentions the source and update frequency ('Source: arXiv, updates daily'), which helps set expectations. However, it does not explicitly state when to use this tool versus alternatives like 'science__crossref' or 'science__openalex', nor does it provide exclusions or prerequisites, leaving some guidance gaps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source ('Crossref (Public)'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }' with explanations of quality scores and citation components). Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description enriches this with practical operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured and front-loaded, with every sentence earning its place: it starts with the core purpose, adds key details (source, updates, return format), and explains the envelope components concisely without unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search functionality with structured returns), rich annotations, 100% schema coverage, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete enough. It covers purpose, usage context, behavioral traits, and return format details, leaving no significant gaps for the agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not add specific meaning to the parameters beyond what the input schema provides. The schema description coverage is 100%, with clear descriptions for 'query' and 'limit', so the baseline score of 3 is appropriate as the schema does the heavy lifting without additional param semantics in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search Crossref for scholarly works by keyword') and resources ('scholarly works'), distinguishing it from sibling tools like 'science__arxiv' or 'science__pubmed' by specifying the data source (Crossref) and the type of information returned (DOIs, titles, authors, citation counts).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Search Crossref for scholarly works by keyword') and implicitly suggests when to use it (for scholarly research), but it does not explicitly state when not to use it or name alternatives among sibling tools (e.g., 'science__arxiv' or 'science__pubmed'), which would be needed for a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the source (Datamuse), licensing (Free / Attribution), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation). This enhances the agent's understanding of reliability and data provenance.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and search types, and the second covers source, licensing, updates, and return format. Every sentence adds essential information without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits, source details, and return format, leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the parameters (query, type, limit). The description adds minimal parameter semantics by mentioning the search types (meaning, sound, spelling, rhyme) but does not provide additional syntax or format details beyond what the schema already specifies. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find words using the Datamuse API — search by meaning, sound, spelling, or rhyme.' It specifies the verb ('Find words'), resource ('Datamuse API'), and scope (four search types), distinguishing it from sibling tools which cover unrelated domains like agriculture, crime, or economics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by listing the four search types (ml, sl, sp, rel_rhy) and mentioning the source (Datamuse) and update frequency (daily). However, it does not explicitly state when to use this tool versus alternatives or include any exclusions, such as when other word-related tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the data source ('iDigBio (CC0 / CC-BY)'), update frequency ('updates daily'), and detailed return structure ('Katzilla envelope { data, quality, citation }') with quality metrics and citation details, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential details (source, updates, return format) in a logical flow. Every sentence adds value—no wasted words—and it efficiently conveys necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with structured output), rich annotations (covering safety and idempotency), 100% schema coverage, and presence of an output schema (implied by return format description), the description is complete. It explains the purpose, source, update frequency, and detailed return structure, compensating well for any gaps without needing to repeat schema details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('query' as scientific name and 'limit' with range/default). The description does not add further meaning beyond the schema, such as query syntax examples or limit implications, but the schema adequately documents parameters, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), resource ('iDigBio biodiversity specimen database'), and scope ('by scientific name'), and distinguishes from siblings by specifying it returns 'digitized natural history collection records'—a unique focus among the listed science tools like arXiv or PubMed, which handle academic papers rather than specimen data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly indicates when to use this tool ('Search the iDigBio biodiversity specimen database by scientific name'), but does not explicitly mention when not to use it or name specific alternatives among siblings (e.g., science__openalex for general research). The context is well-defined, though lacking exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope'), explains quality scores ('freshness/uptime/confidence'), and details citation information ('source URL, license, SHA-256 data hash'). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by additional context in a logical flow. Every sentence adds value: the first states the action and resource, the second specifies sorting and source, and the third details the return structure. It is efficiently written with no redundant information, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It explains the tool's purpose, usage context, behavioral traits, and return format, providing all necessary information for an agent to invoke it correctly without needing to detail output values explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('query' and 'limit') well-documented in the schema. The description does not add any parameter-specific details beyond what the schema provides (e.g., query syntax or limit implications). According to scoring rules, when schema coverage is high (>80%), the baseline score is 3, which applies here as the description does not compensate with extra semantic information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search INSPIRE-HEP for high-energy physics literature.' It specifies the verb ('search'), resource ('INSPIRE-HEP'), and scope ('high-energy physics literature'), distinguishing it from sibling tools like 'science__arxiv' or 'science__pubmed' by focusing on a specific physics database. The mention of 'Returns papers sorted by most recent' further clarifies the output behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching HEP literature from INSPIRE-HEP. It implies usage by stating the source and update frequency ('Source: INSPIRE-HEP (CC0), updates daily'), but does not explicitly mention when not to use it or name alternatives among siblings. This gives adequate guidance without being exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), so the description adds valuable context beyond this. It discloses the data source (NASA Image and Video Library, Public Domain), update frequency ('updates daily'), and details about the return format (Katzilla envelope with quality scores and citation info), enhancing transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. Every sentence adds value: the first defines the action and scope, the second provides source and update info, and the third explains the return format. There is no wasted text, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the description of return values), the description is complete. It covers purpose, source, update frequency, and return format, leaving no significant gaps for the agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the parameters (query, mediaType, limit). The description does not add any additional meaning or syntax details beyond what the schema provides, such as examples of query formats or mediaType usage. Thus, it meets the baseline for high schema coverage without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search'), resource ('NASA's image, video, and audio library'), and scope ('by keyword'). It distinguishes itself from sibling tools by specifying the data source (NASA Image and Video Library) and the unique return format (Katzilla envelope), making it distinct from other science tools like arXiv or PubMed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search NASA's image, video, and audio library by keyword') and mentions the source and update frequency ('updates daily'), which helps in decision-making. However, it does not explicitly state when not to use it or name alternatives among sibling tools, such as other NASA-related tools (e.g., NASA APOD or NASA Asteroids), which could have improved guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, but the description adds valuable context beyond this: it specifies the data source, update frequency (daily), and details about the return structure (quality scores, citation with URL, license, and SHA-256 hash), which helps the agent understand freshness, auditability, and output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format, all in three concise sentences with no wasted words. Every sentence adds essential information, making it efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete enough. It covers purpose, source, update frequency, and return structure, leaving detailed output to the schema, which aligns with best practices.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the 'keyword' and 'limit' parameters. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples of keyword usage or implications of the limit, but it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search National Science Foundation research grants') and resources ('titles, abstracts, funding amounts, awardees, and program names'), and distinguishes it from siblings by specifying its unique domain (NSF awards) and data source, unlike other science tools like arXiv or PubMed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning the data source (National Science Foundation), update frequency (daily), and return format (Katzilla envelope), but does not explicitly state when not to use it or name alternatives among siblings, such as science__nih-reporter or science__openalex for other research databases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (OpenAlex, CC0), update frequency (daily), and details about the return structure ('Katzilla envelope { data, quality, citation }') including quality metrics and citation components. This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format. Every sentence adds critical information (search scope, source details, return structure) with zero waste, making it front-loaded and highly concise for easy comprehension by an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, compensating well for any gaps. With annotations and output schema handling behavioral and output details, the description provides sufficient context for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('query' and 'limit'). The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain query syntax or limit implications). Given the high schema coverage, a baseline score of 3 is appropriate, as the schema adequately documents parameters without needing extra detail in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search'), resource ('OpenAlex catalog of scholarly works — papers, articles, and preprints'), and scope ('by keyword'), distinguishing it from siblings like 'science__arxiv' or 'science__pubmed' by specifying the OpenAlex source and daily updates. It provides a precise verb+resource combination that leaves no ambiguity about the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly suggests usage for keyword-based searches of scholarly works, but it does not explicitly state when to use this tool versus alternatives (e.g., 'science__arxiv' for arXiv-specific searches or 'science__semantic-scholar' for Semantic Scholar). It provides clear context (OpenAlex source, daily updates) but lacks explicit exclusions or named alternatives, which would elevate it to a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source (OSTI.gov), update frequency (daily), and details the return structure (Katzilla envelope with data, quality, citation) including quality metrics and citation components like SHA-256 hash. This enhances transparency about data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, source, and content types; the second details return format and quality metrics. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with quality scoring), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It explains the tool's purpose, source, update frequency, and return structure, compensating well for any gaps. No further details are needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (query and limit). The description does not add any additional parameter semantics beyond what the schema provides, such as query syntax examples or limit implications. Since the schema fully documents the parameters, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Search'), the resource ('U.S. Department of Energy research publications from OSTI.gov'), and the specific content types ('journal articles, technical reports, conference papers, and dissertations'). It clearly distinguishes this as a search tool for DOE publications, differentiating it from other science tools like arXiv or PubMed that cover different sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for searching DOE research publications, with the source specified as OSTI.gov. It mentions the data updates daily, which helps set expectations. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., science__arxiv for arXiv papers).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it specifies the source (PubMed/NCBI), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation data). This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and return type, the second adds source, update frequency, and output format details. Every sentence provides essential information without redundancy, making it front-loaded and zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with parameters), rich annotations covering safety and behavior, and the presence of an output schema (implied by mention of Katzilla envelope), the description is complete. It covers purpose, source, update frequency, and return format adequately, leaving detailed output structure to the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (query and limit). The description does not add any additional meaning or syntax details beyond what the schema provides, such as query formatting examples or result ordering. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search PubMed for biomedical literature') and resource ('PubMed / NCBI'), and distinguishes it from siblings by specifying it returns PubMed IDs and the Katzilla envelope format. It explicitly mentions the source and update frequency, making it distinct from other science tools like arXiv or Semantic Scholar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search PubMed for biomedical literature'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for biomedical queries but lacks explicit exclusions or comparisons to other literature search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source ('Semantic Scholar API Terms'), update frequency ('updates daily'), and the specific return structure ('Katzilla envelope { data, quality, citation }') with details on quality scores and citation metadata. Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description complements them without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality and return data, and the second adds source, update frequency, and output format details. Every sentence adds essential information with zero waste, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with structured output), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage context, behavioral traits, and output structure without needing to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the 'query' and 'limit' parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain query syntax or result ordering). This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search Semantic Scholar for academic papers') and resources ('academic papers'), and distinguishes it from siblings by specifying the data source (Semantic Scholar) and the type of information returned (titles, abstracts, citation counts, links). It explicitly mentions the 'Katzilla envelope' structure, which differentiates it from other search tools that might return raw data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying it's for academic papers and mentioning the data source and update frequency ('updates daily'). However, it does not explicitly state when not to use this tool or name alternatives among siblings (e.g., science__arxiv or science__pubmed), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations: it specifies the source (SHARE/OSF with CC-BY 4.0 license), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation data including SHA-256 hash). This enriches understanding of data freshness, licensing, and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and source, the second details return format and audit features. Every phrase adds value without redundancy, and it is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adds necessary context about the data source, update frequency, and return structure that complements the structured fields effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (query, limit) well-documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or limit implications. Baseline 3 is appropriate since the schema carries the full parameter documentation burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('search the SHARE database') and resources ('research outputs including papers, datasets, and preprints'). It distinguishes from sibling tools by specifying the unique SHARE/OSF source and daily updates, unlike other science tools like arXiv or PubMed that search different repositories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('search the SHARE database for research outputs'), but does not explicitly state when not to use it or name alternatives among the many sibling tools. It implies usage for SHARE-hosted content but lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source ('National Vulnerability Database'), update frequency ('updates daily'), and detailed return format ('Katzilla envelope { data, quality, citation }' with quality metrics and audit information). Annotations already cover safety (readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true), so this extra context about data freshness and output structure is helpful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality, and the second explains the return format and data quality. Every sentence adds value with no wasted words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters), rich annotations covering safety and behavior, and the presence of an output schema, the description is complete. It explains what the tool does, its data source, update frequency, and return format—sufficient for an agent to understand when and how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters. The description mentions 'keyword' but doesn't add semantic details beyond what the schema provides (e.g., search strategies or keyword examples). The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search the NIST National Vulnerability Database for CVEs by keyword') and resource ('CVEs'). It distinguishes itself from sibling tools by specifying its domain (security vulnerabilities) and data source (NIST NVD), which none of the listed siblings cover.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Search the NIST National Vulnerability Database for CVEs by keyword'), but doesn't explicitly mention when not to use it or name specific alternatives. The context implies it's for vulnerability research, but no exclusion criteria or sibling comparisons are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it discloses the data source ('The Space Devs - Launch Library 2'), update frequency ('updates daily'), return format ('Katzilla envelope'), and details like quality scores and citation with SHA-256 hash for audit. This enriches the agent's understanding of data freshness and provenance.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: it opens with the core purpose, specifies the data source and update frequency, and details the return format and its components in a single, information-dense sentence. Every element (source, update cadence, return structure) adds value without redundancy, making it efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, rich annotations, and an output schema implied by the return format description), the description is complete. It covers purpose, data source, update behavior, and output structure, compensating for any gaps. With annotations handling safety and idempotency, and the output schema detailing the 'Katzilla envelope', no critical information is missing for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter ('limit') fully documented in the input schema. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., no details on default behavior or usage tips). Since the schema handles parameter documentation adequately, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get upcoming rocket launch schedule'), resource ('from The Space Devs Launch Library 2 API'), and scope ('upcoming launches'). It distinguishes itself from sibling tools by focusing exclusively on space/astronomy launch data, unlike the diverse siblings covering agriculture, consumer, crime, etc. The mention of 'Space & Astronomy Agent' further contextualizes its domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying it's for 'upcoming rocket launch schedule' and notes the data source updates daily, suggesting it's for current/future launch information. However, it does not explicitly state when not to use it or name alternative tools for historical launch data or other space-related queries (e.g., sibling tools like space__nasa-apod or space__nasa-asteroids), leaving some ambiguity in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source ('NASA APOD (Public Domain)'), update frequency ('updates daily'), and return format details ('Katzilla envelope with quality scores and citation'). Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description complements these with practical usage insights without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: it front-loads the core purpose, includes key details (source, updates, return format) in a single sentence, and avoids redundancy. Every sentence adds essential information without waste, making it efficient for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage, behavioral traits, and output structure adequately, leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting the 'date' parameter. The description adds minimal semantics by mentioning the parameter is optional and for a specific date, but does not provide additional details like format examples or constraints beyond what the schema already states. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get NASA's Astronomy Picture of the Day') and resource ('APOD'), and distinguishes it from siblings by specifying its unique astronomy focus and daily updates. It explicitly mentions the optional date parameter, making the scope precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('optionally for a specific date; omit for today') and implies when to use it (for retrieving APOD data). However, it does not explicitly state when not to use it or name alternatives among siblings, such as other space-related tools like 'nasa-asteroids' or 'nasa-images', which could help differentiate further.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the return format ('Katzilla envelope'), explains quality metrics ('freshness/uptime/confidence'), and details the citation component ('source URL, license, SHA-256 hash'), which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, 100% schema coverage), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by mention of return format), the description is complete. It covers purpose, usage context, behavioral details, and output structure adequately for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (startDate and endDate in YYYY-MM-DD format, max 7 days apart). The description mentions 'date range' but does not add further semantic details beyond what the schema provides, such as default behaviors or constraints not in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('near-Earth asteroid (NEO) data'), specifying the source (NASA's NeoWs API) and scope (date range). It distinguishes itself from sibling tools by focusing exclusively on asteroid data, unlike other space tools like 'nasa-apod' or 'nasa-exoplanets'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('for a given date range') and mentions the data source and update frequency ('updates daily'), which helps determine when to use it. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as for non-asteroid space data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations by disclosing the data source, update frequency, and detailed return structure (quality scores, citation with URL, license, SHA-256 hash). This enriches the agent's understanding without contradicting annotations, though it could mention rate limits or authentication needs for a higher score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured into two sentences: one for the query purpose and data fields, and another for the source, updates, and return format. Every sentence provides essential information without redundancy, making it front-loaded and appropriately sized for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (querying an external API), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers the data source, update frequency, return structure, and audit details, providing sufficient context for an agent to use the tool effectively without needing to explain return values separately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'limit' parameter fully documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as default behavior or usage tips. Given the high schema coverage, a baseline score of 3 is appropriate, as the description relies on the schema for parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose as querying the NASA Exoplanet Archive for confirmed exoplanet data, listing specific fields (name, host star, discovery year, mass, radius). It clearly distinguishes itself from sibling tools by focusing on exoplanet data, unlike other space tools like NASA APOD or asteroids, making the verb+resource combination specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying the data source (NASA Exoplanet Archive), update frequency (daily), and return format (Katzilla envelope). However, it does not explicitly state when not to use this tool or name alternative tools for similar queries, such as other space-related tools in the sibling list, which limits the score from a perfect 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source ('NOAA SWPC'), update frequency ('daily'), and return format details ('Katzilla envelope with quality scores and citation'), which helps the agent understand behavioral traits like data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every sentence adds value without redundancy, and it is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, rich annotations, and an output schema, the description is complete. It covers purpose, source, update frequency, and return format details, providing all necessary context for an agent to invoke the tool correctly without needing to rely solely on structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics, explaining the return structure ('Katzilla envelope { data, quality, citation }') and what 'quality' and 'citation' entail, adding meaningful context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the latest planetary K-index (Kp) data from NOAA SWPC, indicating geomagnetic storm activity.' It specifies the verb ('Get'), resource ('K-index data'), source ('NOAA SWPC'), and distinguishes it from siblings by focusing on solar weather data, unlike other space tools like launch schedules or NASA images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through '[Space & Astronomy Agent]' and mentions the data updates daily, but does not explicitly state when to use this tool versus alternatives (e.g., other space or environmental tools). It provides some implicit guidance but lacks explicit comparisons or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable context beyond annotations: it discloses the data source ('Space-Track.org (US Government / Space-Track Terms)'), update frequency ('updates daily'), and return structure ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation components. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured into two sentences: the first covers purpose and usage, and the second details source, updates, and return format. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema, the description is complete. It covers purpose, usage, behavioral context (source, updates), and return structure, compensating well for any gaps. With annotations and output schema handling safety and return values, no critical information is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the parameters (noradId, limit, searchName). The description adds minimal semantics by mentioning 'Search by NORAD ID or object name', which aligns with the schema but does not provide additional syntax or format details beyond what the schema already specifies. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query', 'Search') and resources ('Space-Track.org', 'satellite orbital data (TLE/GP)'), and distinguishes it from siblings by specifying the unique data source and return format. It explicitly mentions the domain ('Space & Astronomy Agent'), making its role distinct within the server's toolset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool (for satellite orbital data from Space-Track.org) and how to use it (by NORAD ID or object name). However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as other space-related tools like 'space__launch-schedule' or 'space__nasa-asteroids', which slightly limits guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (TheSportsDB), update frequency (daily), tier constraints (Free tier, non-commercial), and the return format (Katzilla envelope with quality scores and citation details), which helps the agent understand behavioral traits like freshness and licensing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and defaults, and the second details the source, constraints, and return format. Every sentence provides essential information without redundancy, making it appropriately sized and front-loaded for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no nested objects), high schema coverage (100%), comprehensive annotations, and presence of an output schema (implied by mention of return format), the description is complete. It covers purpose, usage, source details, and behavioral context, leaving no gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (leagueId and season) fully documented in the schema, including defaults and examples. The description does not add significant meaning beyond the schema, such as explaining parameter interactions or additional constraints, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get all events for a league season'), resource ('from TheSportsDB'), and scope ('Defaults to English Premier League current season'). It distinguishes from sibling tools like 'sports__live-scores' and 'sports__team-info' by focusing on league schedules rather than real-time scores or team details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (to retrieve league event schedules from TheSportsDB) and mentions the default league and season. However, it does not explicitly state when not to use it or name specific alternatives among sibling tools, such as when needing real-time scores versus historical schedules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description adds valuable context beyond these: it specifies the data source (TheSportsDB), its tier and limitations (Free tier, non-commercial, updates daily), and details the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). This enhances the agent's understanding of data freshness, licensing, and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter), rich annotations, and the presence of an output schema (implied by the description of the Katzilla envelope), the description is complete. It covers purpose, source, limitations, and return format, providing sufficient context for the agent to use the tool effectively without needing to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'date' fully documented in the schema as 'Date in ISO format (YYYY-MM-DD)'. The description does not add any further parameter semantics beyond what the schema provides, such as date range constraints or examples, so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('sports events and scores'), specifies the data source (TheSportsDB), and distinguishes it from siblings by focusing on live scores for a specific date rather than schedules or team info. It explicitly mentions the Katzilla envelope structure for the return data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get sports events and scores for a specific date'), mentions the data source and its limitations (Free tier, non-commercial, updates daily), and implies usage for date-specific queries. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., sports__league-schedules or sports__team-info).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies 'No API key required,' 'Source: OpenLigaDB (Public), updates daily,' and details about the return format ('Returns the Katzilla envelope { data, quality, citation } — quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit'). This includes rate limits (implied by 'updates daily'), data freshness, and auditability, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional details in a structured manner. Every sentence adds value: the first states the purpose, the second lists data types and leagues, the third covers API key and source, and the fourth explains the return format. There is no wasted text, and it efficiently conveys necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, 100% schema coverage, annotations, and an output schema), the description is complete enough. It covers the purpose, usage context, behavioral traits (like no API key and daily updates), and return format. With annotations providing safety hints and an output schema likely detailing the Katzilla envelope, the description does not need to explain return values further, making it well-rounded for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with each parameter (type, league, season, matchday) well-documented in the schema. The description does not add significant meaning beyond the schema, as it only mentions data types ('match results, current tables, and team data') and leagues ('Bundesliga (bl1), 2. Bundesliga (bl2), DFB Pokal, Champions League') which are already covered by the schema's enum and descriptions. Given the high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Free German and European football (soccer) data from OpenLigaDB. Get match results, current tables, and team data for Bundesliga (bl1), 2. Bundesliga (bl2), DFB Pokal, Champions League, and many other leagues.' It uses specific verbs ('get') and resources ('match results, current tables, team data'), and distinguishes itself from sibling tools by focusing on football data from OpenLigaDB, unlike other sports tools like 'league-schedules' or 'live-scores'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: for accessing football data from specific leagues (e.g., Bundesliga, Champions League) via OpenLigaDB. It mentions 'No API key required' and 'updates daily,' which are useful usage considerations. However, it does not explicitly state when not to use it or name alternatives among sibling tools, such as 'sports__league-schedules' or 'sports__live-scores,' for different types of sports data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (TheSportsDB), licensing constraints (non-commercial free tier), update frequency (daily), and detailed output structure (Katzilla envelope with quality scores and citation details), which helps the agent understand behavioral traits like data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and output format, all in three efficient sentences with zero waste. Every sentence adds critical information (e.g., data source constraints, output structure), making it appropriately sized and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter), rich annotations (covering safety and idempotency), and the presence of an output schema, the description is complete enough. It adds necessary context like source limitations, update frequency, and output envelope details, which compensates for any gaps and ensures the agent can use the tool effectively without needing further explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'teamName' fully documented. The description does not add any additional meaning or syntax details beyond what the schema provides, such as search behavior (e.g., exact match vs. fuzzy) or examples. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search for team information by name') and resources ('team details, stadium, badge, and social links'), and distinguishes it from siblings by specifying the data source (TheSportsDB) and output format (Katzilla envelope), which no other sports-related tools mention.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Search for team information by name from TheSportsDB'), including source limitations ('Free tier (non-commercial), updates daily'), but does not explicitly state when to use this tool versus alternatives like 'sports__league-schedules' or 'sports__live-scores', nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source ('FCC ECFS'), update frequency ('updates daily'), and detailed return format ('Katzilla envelope { data, quality, citation }' with quality metrics and citation details). While annotations already indicate read-only, non-destructive, idempotent, and open-world characteristics, the description enriches this with practical implementation details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states purpose and source, the second explains the return format. Every element adds value without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, source, update frequency, and detailed return format, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents all three parameters (limit, proceedings, query). The description doesn't add any parameter-specific semantics beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search FCC ECFS'), resource ('public filings and comments on telecommunications proceedings'), and scope ('returns the Katzilla envelope'). It distinguishes itself from sibling tools by focusing specifically on FCC telecommunications proceedings, unlike other tools covering agriculture, consumer, crime, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('search FCC ECFS for public filings and comments on telecommunications proceedings'). However, it doesn't explicitly mention when NOT to use it or name specific alternative tools for related searches, though the sibling list shows many other government data tools that serve different domains.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source ('Eurostat'), update frequency ('updates monthly'), and return format ('Katzilla envelope { data, quality, citation }' with details on quality scores and citation components). Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description enhances this with practical usage insights like data freshness and audit features.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded and efficiently structured: it starts with the core purpose, adds key details (coverage, source, updates), and ends with return format specifics. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (trade data retrieval), rich annotations (read-only, idempotent, etc.), 100% schema coverage, and presence of an output schema, the description is complete enough. It covers purpose, data scope, source, update frequency, and return format, addressing key contextual needs without needing to explain parameters or output details already handled by structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents all three parameters (geo, partner, indic_et) with descriptions and enums. The description does not add significant parameter semantics beyond what the schema provides, such as explaining interactions between parameters or default behaviors. It mentions the data scope but not parameter-specific details, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving 'EU international trade in goods data from Eurostat' with specific details like 'monthly and annual imports and exports by partner country' and coverage of 'intra-EU and extra-EU trade flows in millions of euros.' It distinguishes itself from sibling tools by focusing on Eurostat trade data, unlike other trade tools like 'trade__usitc' or economic tools like 'economic__comtrade,' which cover different sources or aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for accessing Eurostat trade data with specific parameters like geo, partner, and indicator. However, it does not explicitly state when not to use it or name alternatives among sibling tools (e.g., 'economic__comtrade' for broader trade data or 'economic__wto-trade' for WTO data), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, indicating a safe, repeatable, and open-ended search operation. The description adds valuable context beyond annotations: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 data hash'), which are not covered by annotations. No contradiction with annotations is present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, and the second details the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a search tool with annotations and an output schema), the description is complete. It covers purpose, source, update frequency, and detailed return format, compensating for any gaps. With annotations providing safety hints and an output schema likely defining the envelope structure, the description adds sufficient context for effective use without needing to explain return values explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'query' documented as 'Search keyword for HTS tariff items'. The description adds minimal parameter semantics beyond the schema, only implying keyword-based search in the opening sentence. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not significantly enhance parameter understanding but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search'), resource ('U.S. International Trade Commission Harmonized Tariff Schedule (HTS)'), and scope ('tariff codes, descriptions, and duty rates by keyword'). It distinguishes itself from siblings by focusing on trade tariff data, unlike other tools in the list (e.g., agriculture, consumer, economic datasets).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('Search... for tariff codes, descriptions, and duty rates by keyword') and mentions the source and update frequency ('Source: USITC (Public Domain), updates monthly'), which helps set expectations. However, it does not explicitly state when not to use it or name specific alternatives among siblings, though the trade focus implies differentiation from non-trade tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world hints, but the description specifies the source (Bureau of Transportation Statistics), update frequency (daily), and the return format (Katzilla envelope with data, quality, citation including source URL, license, and SHA-256 hash). This provides practical details on data freshness, auditability, and output structure that annotations do not cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with two sentences that efficiently convey the tool's purpose, data source, update frequency, and return format. Every sentence adds essential information without redundancy, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a data retrieval tool with one parameter), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers the tool's purpose, data source, update behavior, and output structure, providing sufficient context for an agent to use it effectively without needing to explain return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'limit' parameter clearly documented as 'Number of records to return' with a default of 20. The description does not add any further details about parameters, such as typical usage or constraints, but since the schema already provides complete information, the baseline score of 3 is appropriate as the description does not compensate or add extra value in this area.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get U.S. transportation statistics from the Bureau of Transportation Statistics' with specific examples like 'airline on-time performance, traffic data, and transport metrics.' It distinguishes itself from sibling tools by specifying the data source (Bureau of Transportation Statistics) and domain (transportation), which is distinct from other categories like agriculture, consumer, crypto, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for accessing U.S. transportation statistics from a specific public source that updates daily. It implies usage by mentioning the data source and update frequency, but does not explicitly state when not to use it or name alternative tools for similar data, such as other transport-related sibling tools like 'transport__bc-ferries' or 'transport__eurostat-transport.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source ('FIPE / Parallelum API (Public)'), update frequency ('updates daily'), and return format ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation contents. Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description appropriately supplements with operational details without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, and the second details the source, update frequency, and return format. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (read-only, non-destructive, idempotent, open-world), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return structure, compensating well for any gaps. With an output schema handling return values, the description need not explain them further, making it sufficiently comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the two parameters (type with enum and default, brandId with its purpose). The description adds no additional parameter semantics beyond what the schema provides, such as examples or edge cases, but this is acceptable given the high schema coverage, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get Brazilian vehicle pricing data', 'List brands or get model details') and resources ('from FIPE', 'for cars, motorcycles, and trucks'). It distinguishes itself from sibling tools by focusing on Brazilian vehicle pricing data, unlike other transport tools that cover different regions or data types (e.g., transport__bc-ferries, transport__bts-stats).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('Get Brazilian vehicle pricing data from FIPE'), but does not explicitly state when not to use it or name specific alternatives. It implies usage for vehicle pricing queries in Brazil, but lacks explicit exclusions or comparisons to other tools that might handle similar data (e.g., consumer or economic tools).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it specifies the data source (iRail API with CC0 1.0 license), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation including SHA-256 hash). This enhances transparency about data provenance and output structure, though it doesn't cover rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every element (e.g., API source, update frequency, output structure) adds value without redundancy. It is front-loaded with core functionality and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and output format, compensating for any gaps. With annotations and output schema handling safety and return values, no additional details are needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear parameter descriptions for 'from' and 'to'. The description adds minimal parameter semantics beyond the schema, only implying that parameters are used for connections (not for station lists). It doesn't clarify syntax, examples beyond the schema, or how parameters interact (e.g., both optional for station list). Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Get Belgian railway (NMBS/SNCB) station list or connections between stations using the iRail API.' It specifies the exact action (get), resource (station list or connections), and scope (Belgian railway via iRail API). This clearly distinguishes it from sibling transport tools like 'transport__bc-ferries' or 'transport__swiss-transport' which cover different transport modes or regions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for Belgian railway data (station lists or connections). However, it does not explicitly mention when not to use it or name specific alternatives among siblings (e.g., for non-Belgian transport data). The guidance is sufficient for basic usage but lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it discloses the data source (Toronto TTC / UmoIQ), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation info including SHA-256 hash). This enriches the agent's understanding of data provenance and reliability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second explains the return format and its components. Every sentence adds critical information without redundancy, and it's front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, read-only operation), rich annotations, and the presence of an output schema (implied by the description of the return format), the description is complete. It covers purpose, usage context, behavioral traits, and output structure, leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter (stopId) fully documented in the schema. The description adds marginal value by explaining that omitting stopId returns a route list, while including it triggers arrival predictions. However, it doesn't provide additional syntax or format details beyond what the schema already states, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get Toronto Transit Commission (TTC) route list or real-time arrival predictions for a stop') and distinguishes it from siblings by specifying it's for TTC data only, unlike other transport tools like 'transport__bc-ferries' or 'transport__swiss-transport'. It explicitly mentions the resource (TTC routes/arrivals) and scope (Toronto).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (for TTC route lists or real-time arrival predictions) and implies usage based on parameter presence (omit stopId for route list, include for predictions). However, it doesn't explicitly state when not to use it or name alternatives among siblings, though the TTC focus naturally differentiates it from other transport tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already provide excellent behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true). The description adds valuable context beyond these annotations: it specifies the data source (NHTSA), update frequency (daily), and describes the return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). This provides important operational context that annotations alone don't convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality and parameters, the second provides source metadata and output format details. Every element serves a purpose - there's no redundant information, and it's appropriately front-loaded with the primary use case.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations (readOnly, non-destructive, idempotent, openWorld), 100% schema coverage, and the presence of an output schema, the description provides excellent contextual completeness. It covers purpose, source, update frequency, and output structure - addressing all key aspects an agent needs to understand when and how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage with clear parameter documentation (make, model, year with examples and defaults). The description mentions these parameters ('by make, model, and year') but doesn't add significant semantic value beyond what the schema already provides. It doesn't explain parameter interactions, constraints, or provide additional usage context for the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search vehicle safety complaints'), resource ('filed with the National Highway Traffic Safety Administration'), and scope ('by make, model, and year'). It distinguishes from sibling tools like 'transport__nhtsa-recalls' and 'transport__nhtsa-safety-ratings' by focusing specifically on complaints rather than recalls or ratings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Search vehicle safety complaints... by make, model, and year') and mentions the data source and update frequency. However, it doesn't explicitly state when NOT to use it or name specific alternatives among sibling tools, though the context implies it's for complaint data rather than recalls or ratings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source (NHTSA, Public Domain), update frequency (daily), and details about the return structure (quality scores for freshness/uptime/confidence, citation with URL, license, SHA-256 hash). This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, and the second details the return format and source metadata. Every sentence adds essential information (e.g., rating types, update frequency, return structure) without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, read-only operation), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return format, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear defaults and examples for make, model, and year. The description does not add any parameter-specific semantics beyond what the schema provides, such as validation rules or usage tips. With high schema coverage, the baseline score of 3 is appropriate as the schema carries the full burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with a specific verb ('Get') and resource ('NHTSA NCAP safety ratings for vehicles'), detailing the rating types included (overall, frontal crash, side crash, rollover). It clearly distinguishes from sibling tools like 'transport__nhtsa-complaints' and 'transport__nhtsa-recalls' by focusing on safety ratings rather than complaints or recalls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying the data source (NHTSA), update frequency (daily), and return format (Katzilla envelope). However, it does not explicitly state when to use this tool versus alternatives (e.g., other vehicle data tools) or any prerequisites, leaving some guidance implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (Swiss Public Transport API, Open Data), real-time updates, and details about the return format (Katzilla envelope with quality scores and citation). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description effectively supplements with operational details without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source and return details in a structured manner. Every sentence adds value (e.g., specifying the API, real-time updates, and return format), with no wasted words, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search and connection functionality), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, behavioral traits, and return format, providing sufficient context without needing to explain parameters or output values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents the parameters ('query', 'from', 'to'). The description does not add further meaning or syntax details for these parameters beyond what the schema provides, such as examples or constraints, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('search Swiss public transport stations' and 'get connections between locations'), identifies the resource (Swiss Transport API), and distinguishes it from siblings by specifying its domain (Swiss public transport) and data source (SBB, BLS, PostBus, etc.), which is unique among the listed transport tools like 'transport__bc-ferries' or 'transport__uk-transport'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating it's for searching stations or getting connections, but it does not explicitly mention when to use this tool versus alternatives (e.g., other transport tools like 'transport__irail' or 'transport__uk-transport') or provide exclusions. The guidance is clear but lacks explicit sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits, but the description specifies the data source ('Department for Transport'), update frequency ('updates daily'), license ('Open Government Licence'), and output structure ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation contents. This enhances understanding of data freshness, reliability, and auditability without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured, with three sentences that efficiently cover purpose, source, and output format without redundancy. Each sentence adds critical information: the first defines scope and data types, the second specifies source and updates, and the third details the return structure and its components. There is no wasted text, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a data retrieval tool with annotations covering safety and idempotency, and an output schema (implied by 'Has output schema: true'), the description is complete. It covers the tool's purpose, data source, update frequency, license, and detailed output structure, including quality metrics and citation details. This provides sufficient context for an agent to use the tool effectively without needing to explain return values, as the output schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents the two parameters ('query' and 'limit') with descriptions and defaults. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples of effective queries or guidance on limit selection. This meets the baseline score of 3, as the schema carries the full burden of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with specific verbs ('returns') and resources ('UK transport statistics from the Department for Transport'), listing concrete data types like 'road traffic, vehicle registrations, bus usage, rail passenger numbers, and road safety data'. It clearly distinguishes itself from sibling tools by specifying its geographic and thematic scope (UK transport), unlike other transport tools like 'transport__bts-stats' (US) or 'transport__eurostat-transport' (EU).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for accessing UK transport statistics from the Department for Transport, with daily updates. It implies usage for data retrieval rather than analysis or filtering, but does not explicitly state when not to use it or name specific alternatives among sibling tools, such as 'transport__eurostat-transport' for EU data or 'transport__bts-stats' for US data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, indicating safe, repeatable operations. The description adds valuable context beyond this: it specifies that the service is 'Free to use' and 'updates daily', describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality metrics ('freshness/uptime/confidence') and citation details, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional details in a structured manner. Every sentence adds value: service verification, source information, return format, and quality explanation, with no wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage context, behavioral details, and output structure, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('data' and 'size'). The description adds minimal semantic value beyond the schema, only implying that 'data' can be 'any text or URL', which is already covered. With high schema coverage, the baseline score of 3 is appropriate as the schema does most of the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Generate a QR code image URL') and resources ('for any text or URL'), and it distinguishes itself from siblings by focusing on QR code generation, which is unique among the listed utilities tools like fetch-url, rdap-whois, unit-conversion, and url-shortener.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('Generate a QR code image URL for any text or URL') and mentions the service source ('goQR.me'), but it does not explicitly state when not to use this tool or name specific alternatives among siblings, such as url-shortener for URL handling, which could be relevant.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the return format ('Katzilla envelope'), details on quality scoring ('freshness/uptime/confidence'), and citation information ('source URL, license, SHA-256 hash'). This enriches the agent's understanding of the tool's behavior without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and key return fields, the second explains the return format and data quality. Every sentence adds value, with no redundant information, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, full schema coverage, annotations covering safety, and an output schema implied by the return format description), the description is complete. It covers purpose, usage context, behavioral details, and output structure, leaving no gaps for the agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'domain' parameter clearly documented. The description does not add any additional semantic details about parameters beyond what the schema provides (e.g., no examples of valid domains beyond 'example.com' or edge cases). Given the high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Look up domain registration information using the RDAP protocol (successor to WHOIS).' It specifies the verb ('look up'), resource ('domain registration information'), and protocol ('RDAP'), distinguishing it from sibling tools like 'utilities__fetch-url' or 'utilities__qr-code' that serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for domain registration lookups via RDAP. It mentions the data source ('RDAP.org') and update frequency ('daily'), which helps set expectations. However, it does not explicitly state when not to use it or name alternatives (e.g., traditional WHOIS tools), keeping it from a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the source ('Local Computation (N/A), updates daily'), describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality scoring and citation details. Annotations already cover safety (readOnlyHint, destructiveHint) and idempotency, so the description complements them with operational and output behavior without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core purpose, followed by supported units, source information, and return format. Each sentence adds essential information without redundancy, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage context, behavioral traits, and output details, leaving no significant gaps for the agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents the three parameters (value, from, to). The description adds minimal parameter semantics by listing example units (e.g., 'km', 'lbs', 'C') in the supported categories, which provides context but does not significantly enhance the schema's details. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Convert between common units of measurement') and resources (listing multiple measurement categories like length, weight, volume, speed, temperature). It distinguishes itself from sibling tools (e.g., utilities__fetch-url, utilities__qr-code) by focusing exclusively on unit conversion, making its scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool by listing supported measurement categories and units, which implies usage for converting between these specific units. However, it does not explicitly state when not to use it or name alternatives (e.g., for currency conversion or other utilities), leaving some guidance implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the source ('CourtListener RECAP Archive / Free Law Project'), update frequency ('updates daily'), return format ('Katzilla envelope'), and quality metrics ('freshness/uptime/confidence'). Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description enriches this with operational details like data source and audit features (SHA-256 hash).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by usage context, return details, and source information. It is moderately concise but includes some dense technical details (e.g., 'Katzilla envelope') that are necessary for completeness. Minor room for tightening exists, but overall it's efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple parameters), rich annotations, and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is highly complete. It covers purpose, usage scenarios, behavioral traits, source, update frequency, and return format, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain 'party_name' matching behavior beyond the schema's 'case-insensitive substring match'). Baseline score of 3 is appropriate as the schema carries the full burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search RECAP for federal dockets where a named party appears' with specific verbs ('search', 'returns') and resources ('federal dockets', 'party'). It distinguishes from sibling tools like 'crime__recap-docket' or 'crime__recap-search' by focusing on party-based docket searches rather than general docket/document retrieval or broader searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'adverse-party checks, conflict-of-interest review, tracking a litigant across jurisdictions.' It also clarifies what it does not do ('Returns one hit per docket (not per filing)'), helping differentiate it from tools that might return per-filing results. No explicit alternatives are named, but the context signals its specialized use case effectively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it specifies the data source ('CourtListener RECAP Archive / Free Law Project'), update frequency ('updates daily'), return format ('Katzilla envelope { data, quality, citation }'), and details about quality scores and citation metadata. This provides practical behavioral information not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, parameters, and return content; the second details source, update frequency, and output format. Every element serves a clear purpose with zero redundant information, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage, source, update frequency, and output structure, leaving no gaps for agent understanding. The output schema handles return value details, so the description appropriately focuses on higher-level context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds minimal semantic context beyond the schema—it mentions the parameter relationship ('Use this OR (court + docket_number)') and provides an example ('e.g. 'caDC' + '21-5166''), but doesn't explain parameter implications or usage nuances. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fetch a single federal docket'), resource ('from the RECAP Archive'), and scope ('by CourtListener docket id, or by court code + docket number'). It distinguishes from sibling tools like 'crime__recap-search' by specifying this retrieves a single docket rather than searching, and from 'crime__recap-document' by focusing on docket sheets rather than documents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use this tool ('Fetch a single federal docket') and provides clear alternatives for parameter input ('Use this OR (court + docket_number)'). The description also references 'recap-search results' as a source for docket_id, guiding users to sibling tools when appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it specifies the data source ('CourtListener RECAP Archive / Free Law Project'), update frequency ('updates daily'), return format ('Katzilla envelope'), and details about quality scores and citation metadata. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by return details, source information, and output structure. Every sentence adds value: the first defines the tool, the second lists returns, the third specifies source and updates, and the fourth explains the envelope format. No redundant or vague statements.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (fetching legal documents), the description is complete. It covers purpose, usage, source, update frequency, and output structure. With annotations covering safety/idempotency and an output schema existing (implied by 'Returns the Katzilla envelope'), the description doesn't need to explain return values in depth. It provides all necessary context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description mentions 'plain-text extraction' and 'metadata only', which aligns with the 'include_text' parameter but doesn't add new semantic details beyond what the schema provides. It also references 'document_id' but doesn't elaborate further. Baseline 3 is appropriate given the comprehensive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Fetch a single filing'), the resource ('from RECAP by its CourtListener document id'), and the return content ('document metadata, plain-text extraction, download URL'). It clearly distinguishes from sibling tools like 'recap-search' (which searches) and 'recap-docket' (which fetches dockets), specifying this tool retrieves individual documents by ID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('Fetch a single filing... by its CourtListener document id') and how to obtain the ID ('Get this from recap-search results or from the docket_entries of a recap-docket response'). It distinguishes from alternatives by focusing on single-document retrieval, unlike search or docket tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond this: it specifies the data source (CourtListener RECAP Archive), update frequency ('updates daily'), return format ('Katzilla envelope { data, quality, citation }'), and explains quality scoring and citation details. No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: it starts with the core purpose, lists key filters, explains the return format and follow-up action, and ends with source and update details. Every sentence adds value without redundancy, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, required query), rich annotations, and existence of an output schema, the description is complete. It covers purpose, usage guidance, behavioral context (source, updates, return format), and integration with sibling tools, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema. The description adds some semantic context by mentioning filtering capabilities ('Filter by court, filing date range, party name, or nature of suit') and linking to external documentation for natureOfSuit, but doesn't provide significant additional parameter meaning beyond what's already in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool performs a 'Full-text search across the RECAP Archive (federal court dockets mirrored from PACER by Free Law Project)', specifying both the verb ('search') and resource ('RECAP Archive'). It clearly distinguishes from sibling tools like 'crime__recap-docket' by explaining this returns docket entries for further lookup, not full docket sheets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: 'feed the docket_id into recap-docket for the full sheet' directly names the sibling tool for follow-up actions. It also outlines filtering capabilities (court, date range, party name, nature of suit) that define appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations by specifying the source (Blockchain.info with MIT License), real-time updates, and the return format ('Katzilla envelope' with quality scores and citation details including SHA-256 hash). This enhances transparency about data provenance and structure without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and key metrics, and the second explains the source and return format. Every sentence adds essential information (e.g., data source, licensing, output structure) without redundancy, making it front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, annotations covering safety, and an output schema implied by the description of the 'Katzilla envelope'), the description is complete. It covers purpose, source, licensing, update frequency, and output structure, providing all necessary context for an agent to understand and use the tool effectively without needing to reference other fields excessively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's function and output. This aligns with the baseline expectation for zero-parameter tools, where the description compensates by detailing behavior and output.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Get'), resource ('Bitcoin network statistics'), and specific metrics ('market price, hash rate, transaction count, and block time'), making the purpose highly specific. It distinguishes from sibling crypto tools by specifying the data source (Blockchain.info) and type of statistics, unlike tools like crypto__coinlore-stats or crypto__etherscan-price which focus on different cryptocurrencies or specific data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying the data source (Blockchain.info) and that it returns real-time Bitcoin network statistics. However, it does not explicitly state when not to use this tool or name alternatives among siblings (e.g., crypto__coinpaprika for broader cryptocurrency data), leaving some guidance implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, but the description adds valuable context beyond this. It specifies the source (Coinlore Free API), update frequency (real-time), and details about the return format (Katzilla envelope with quality scores and citation for audit). This enhances transparency about data freshness, reliability, and auditability, which annotations do not cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional details about the source and return format. Every sentence adds value: the first defines the action, the second specifies source and updates, and the third explains the output structure. There is no wasted text, making it highly efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, rich annotations, and an output schema), the description is complete. It covers purpose, source, update behavior, and output format in detail. With annotations handling safety and idempotency, and an output schema likely defining the return structure, the description fills all necessary gaps, providing a comprehensive understanding for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not discuss parameters, which is appropriate. It earns a baseline of 4 because it effectively compensates by providing context about the tool's operation and output, ensuring the agent understands how to invoke it correctly without parameter confusion.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('Get'), resource ('global cryptocurrency market statistics'), and source ('Coinlore'), making the purpose specific and clear. It distinguishes from sibling crypto tools by specifying the exact data source and type of statistics (global market stats), unlike tools like 'crypto__blockchain-stats' or 'crypto__coinpaprika' which might focus on different aspects or sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for real-time global cryptocurrency market statistics from Coinlore. It mentions the source and update frequency, which helps in decision-making. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as when other crypto tools might be more appropriate for different data types or sources.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the real-time update nature, the specific return structure ('Katzilla envelope'), and details like quality scores and citation with SHA-256 hash for audit, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and output, the second details the return format and source. Every sentence adds critical information without redundancy, making it front-loaded and zero-waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, rich annotations, and an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and output details, providing all necessary context for an agent to invoke it correctly without needing to explain return values redundantly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on output semantics. It adds meaning by explaining the return format ('Katzilla envelope') and its components, which compensates for the lack of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get current Ethereum gas prices from Etherscan gas oracle.' It specifies the verb ('Get'), resource ('Ethereum gas prices'), source ('Etherscan'), and output format ('safe, proposed, and fast gas prices in Gwei'), distinguishing it from sibling tools like crypto__etherscan-balance or crypto__etherscan-price which handle different data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'real-time' updates and the source 'Etherscan,' suggesting it's for current gas price queries. However, it does not explicitly state when to use this tool versus alternatives (e.g., crypto__mempool-fees for Bitcoin fees) or any prerequisites, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description adds valuable context beyond these. It discloses that the tool 'updates real-time' (indicating freshness), describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality metrics ('freshness/uptime/confidence') and citation details ('source URL, license, and a SHA-256 data hash for audit'). This enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by source and update frequency, and then details on the return format. Every sentence adds essential information without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, annotations covering safety and behavior, and an output schema implied by the description), the description is complete. It explains what the tool does, its source, update frequency, and return format with quality and citation details, providing all necessary context for an agent to use it effectively without gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not mention any parameters, which is appropriate. It adds value by explaining the output structure and data sources, compensating for the lack of input complexity. Baseline is 3 for high schema coverage, but the description's output details elevate it slightly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Get the current ETH price in USD and BTC from Etherscan.' It specifies the verb ('Get'), resource ('current ETH price'), and distinguishes it from sibling tools like 'crypto__etherscan-balance' or 'crypto__etherscan-gas' by focusing on price data rather than balances, gas fees, or transactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Source: Etherscan (Etherscan Terms), updates real-time.' This indicates when to use it (for real-time ETH price data from Etherscan) and implies it's for cryptocurrency price queries. However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'crypto__coinpaprika' for other crypto prices.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide. While annotations indicate read-only, non-destructive, idempotent, and open-world characteristics, the description adds that the data 'updates real-time,' specifies the source licensing (AGPL-3.0), and details the output structure (Katzilla envelope with quality scores and citation including SHA-256 hash). This provides important operational context about data freshness, auditability, and source attribution.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that pack substantial information: the core functionality, data source, output format, and metadata about quality and auditability. Every element serves a purpose with no wasted words, and key information is front-loaded in the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, comprehensive annotations, and an output schema (implied by the detailed description of the return format), the description provides complete context. It covers purpose, source, licensing, real-time nature, output structure, quality metrics, and audit features—everything needed to understand and use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately acknowledges this by not discussing parameters and instead focuses on what the tool does without inputs. It correctly states that the tool fetches current fees without requiring any parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get current Bitcoin recommended transaction fees from Mempool.space' with specific details about what it returns ('fee rates in sat/vB for different confirmation targets') and distinguishes it from siblings by specifying the data source (Mempool.space) and output format (Katzilla envelope). It uses specific verbs ('Get', 'Returns') and identifies the exact resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for obtaining Bitcoin transaction fees from a specific real-time source. It mentions the source (Mempool.space) and that it updates real-time, which helps differentiate it from potential alternatives. However, it doesn't explicitly state when NOT to use it or name specific alternative tools among its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source, update frequency ('updates daily'), and return format ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation contents. Annotations already indicate read-only, non-destructive, idempotent, and open-world traits, so the description complements these with operational specifics without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and usage, the second details the source and return format. Every sentence adds critical information (e.g., source, update frequency, output structure) with no redundant or vague phrasing, making it front-loaded and highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, high schema coverage, annotations present, output schema exists), the description is complete. It covers purpose, usage, behavioral traits, source details, and output format, providing all necessary context for an agent to invoke the tool correctly without needing to rely solely on structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'reference' fully documented in the schema. The description mentions the parameter indirectly by describing supported reference formats ('single verses, ranges, and multiple references'), but adds minimal semantic value beyond what the schema already provides. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Retrieve Bible verses and passages by reference.' It specifies the exact action ('retrieve'), resource ('Bible verses and passages'), and input method ('by reference'), and distinguishes itself from sibling tools by focusing on Bible content retrieval, unlike other culture tools like art, poetry, or dictionary APIs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage guidance: it specifies when to use this tool ('Supports single verses, ranges, and multiple references') and implicitly when not to use it (e.g., for non-Bible content or other reference types). It also mentions the source ('Bible API (Public Domain (World English Bible))'), which helps differentiate it from alternatives like Quran or other religious texts in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description adds valuable behavioral context beyond this: it specifies the data source ('Carbon Intensity API (National Grid ESO)'), update frequency ('updates daily'), and return structure ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation content. This enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential details in a structured manner (source, update frequency, return format). Every sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, annotations provided, output schema exists), the description is complete. It covers purpose, source, update frequency, and return structure, compensating for any gaps. With an output schema, it need not explain return values in detail, making this description sufficient for the agent's needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the schema fully documents the inputs. The description adds no parameter-specific information, which is appropriate, but it does imply the tool requires no inputs for its operation, aligning with the schema. Baseline is 4 for zero parameters, as no additional semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with specific verbs ('Get the current carbon intensity') and resources ('UK electricity generation from the National Grid ESO'), clearly distinguishing it from sibling tools like environment__climate-trace or environment__openaq by focusing on UK electricity carbon intensity from a specific source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get the current carbon intensity of UK electricity generation'), implying it's for real-time or daily-updated UK-specific data. However, it does not explicitly state when not to use it or name alternatives among siblings, such as environment__climate-trace for broader climate data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (EPA AQS, Public Domain), update frequency (daily), and detailed return format (Katzilla envelope with quality scores and citation details including SHA-256 hash). While annotations cover read-only, non-destructive, idempotent, and open-world properties, the description enriches this with implementation-specific details that help the agent understand data provenance and structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality and source, the second details the return format. Every element (source, update frequency, return structure) adds value without redundancy. It's front-loaded with the main purpose and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, read-only operation with annotations), the description provides complete context: purpose, data source, update frequency, and detailed return format. With output schema existing, it doesn't need to explain return values, but it usefully describes the envelope structure. This is fully adequate for this straightforward lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately notes there are no required inputs by not discussing parameters, which is correct for this parameterless tool. It focuses instead on what the tool does and returns, which is the right emphasis.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('List') and resource ('all US states available in the EPA Air Quality System'), and distinguishes it from siblings by specifying the data source (EPA AQS) and scope (US states only). It explicitly mentions what it returns (Katzilla envelope structure), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (to get US state data from EPA AQS, updated daily), but does not explicitly mention when not to use it or name specific alternatives among the many sibling tools. The source and update frequency guidance helps, but sibling differentiation is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: 'Rich card data with images, prices, and legality,' 'Source: Scryfall (Scryfall Terms), updates daily,' and details about the return envelope structure (quality scores, citation with URL/license/hash). This provides important operational context about data freshness, licensing, and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: first sentence states purpose and capabilities, second describes data richness, third covers source and update frequency, fourth explains return format. Every sentence adds value with zero waste, and it's appropriately front-loaded with core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has comprehensive annotations, 100% schema coverage, and an output schema (implied by the return envelope description), the description provides complete context. It covers purpose, usage modes, data characteristics, source attribution, update frequency, and return format - addressing all necessary aspects for this search/retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with the query parameter well-documented in the schema. The description adds minimal parameter semantics beyond the schema, only implying the query supports 'Scryfall syntax' and that omission triggers random card mode. This meets the baseline 3 since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search Magic: The Gathering cards via Scryfall' with specific verbs (search, returns) and resource (cards). It distinguishes from siblings by specifying the domain (Magic: The Gathering) and data source (Scryfall), which none of the other fun__* tools cover.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Supports full-text search or returns a random card' and specifies 'Omit [query] for a random card' in the schema. It clearly indicates when to use (search or random retrieval) and how to trigger each mode, with no alternatives needed among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it notes the data source ('Data.gov (Public Domain)'), update frequency ('updates daily'), and details the return structure ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation contents. This enriches the agent's understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidance, source details, and return format explanation. Each sentence is necessary and contributes to understanding, with no wasted words, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (data retrieval with quality metrics), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, usage prerequisites, source information, update frequency, and return structure details, providing all necessary context for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting the single required parameter 'dataset_id'. The description adds minimal semantic context by mentioning 'Data.gov dataset ID (CKAN package UUID) from catalog search', which aligns with the schema but doesn't provide significant additional meaning. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Download and serve any validated Data.gov dataset as structured JSON.' It specifies the action ('download and serve'), resource ('Data.gov dataset'), and output format ('structured JSON'), distinguishing it from sibling tools like 'datagov' (search) and other government data tools by focusing on dataset retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use the datagov action to search for datasets and get their IDs first.' It names the alternative tool ('datagov') and specifies the prerequisite step, clearly indicating when to use this tool versus others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable context beyond annotations: it specifies the data source (GovInfo/GPO), update frequency (daily), and details the return envelope structure (Katzilla with quality scores and citation info including SHA-256 hash). This enriches the agent's understanding of data provenance and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality and return data, and the second adds source, update frequency, and envelope details. Every sentence adds critical information (e.g., data formats, quality metrics, audit trail) with zero wasted words, making it highly front-loaded and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, 100% schema coverage, annotations present, output schema exists), the description is complete. It covers purpose, usage, source context, return structure, and data quality—compensating well for any gaps. With an output schema handling return values, no further detail is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'packageId' fully documented in the schema (including format examples and reference to govinfo-search). The description adds minimal extra semantics by mentioning package types (e.g., 'BILLS-117hr3684enr') but doesn't provide additional syntax or constraints beyond the schema. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose with a specific verb ('Fetch') and resource ('full metadata summary for a single GovInfo package'), and distinguishes it from siblings by specifying it's for a single package by packageId (unlike broader search tools like govinfo-search). It clearly identifies the types of resources covered (court opinion, public law, bill, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('Fetch the full metadata summary for a single GovInfo package... by packageId') and when not to use it (implied: not for searching multiple packages). It references an alternative ('Get these from govinfo-search results') for obtaining packageIds, establishing clear context for usage versus sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide: it discloses the return format ('Returns the Katzilla envelope { data, quality, citation }'), explains quality scoring ('quality scores freshness/uptime/confidence'), and describes citation details ('citation carries the source URL, license, and a SHA-256 data hash for audit'). While annotations cover read-only, non-destructive, idempotent, and open-world aspects, the description enriches understanding of output behavior and data characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences, each serving a distinct purpose: (1) defines the search scope and basic return, (2) provides sibling tool guidance, (3) describes source and detailed return format. There's no redundant information, and key details are front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search across multiple government collections), the description provides excellent contextual completeness. It explains the search scope, return format, sibling relationships, data source, update frequency, and detailed output structure. With comprehensive annotations and an output schema (implied by 'Has output schema: true'), the description adds exactly the right amount of supplementary context without duplicating structured information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. However, it does provide context about the search scope ('across all... collections') and result format, which relates to parameters indirectly. The baseline score of 3 is appropriate when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search across all U.S. Government Publishing Office (GPO) GovInfo collections'), identifies the resources ('USCODE, CFR, PLAW, FR, USCOURTS, CREC, BILLS, etc.'), and distinguishes it from sibling tools by explicitly naming alternatives ('use govinfo-package, govinfo-granule, or govinfo-content for the underlying documents'). This provides complete purpose clarity with sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: 'use govinfo-package, govinfo-granule, or govinfo-content for the underlying documents.' It also clarifies the scope ('Returns packageId / granuleId references plus basic metadata') and source context ('Source: GovInfo (GPO) (Public Domain), updates daily'), giving comprehensive usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations by specifying the update frequency ('updates daily'), the return format ('Katzilla envelope'), and details about quality scores and citation components. This enhances understanding of the tool's behavior without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every sentence provides essential information with zero waste, making it highly concise and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, rich annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return format, providing all necessary context for an agent to understand and use the tool effectively without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics, explaining the return structure ('Katzilla envelope') and its components (data, quality, citation), which adds meaningful context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific verb ('List available datasets') and resource ('from the HealthCare.gov open data portal'), distinguishing it from siblings by specifying the exact data source. It explicitly mentions the data source and update frequency, making the purpose unambiguous and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (to list datasets from HealthCare.gov with daily updates), but does not explicitly state when not to use it or name specific alternatives among the many sibling tools. The context is sufficient for basic usage but lacks explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond what annotations provide: it specifies the update frequency ('updates monthly'), describes the return format structure ('Katzilla envelope { data, quality, citation }'), and explains what the quality scores and citation contain. While annotations cover read-only, non-destructive, idempotent, and open-world characteristics, the description adds practical implementation details about data freshness and auditability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and well-structured: a single sentence states the core functionality, followed by essential details about source, update frequency, and return format. Every element earns its place, with no redundant information. The bracketed explanation of the Katzilla envelope components is efficiently presented.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, rich annotations, output schema exists), the description is complete. It covers purpose, source, update frequency, return format structure, and data quality/audit information. With an output schema presumably defining the Katzilla envelope structure, the description doesn't need to explain return values in detail, making this appropriately comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately notes there are no input parameters needed ('Get daily exchange rates' implies no filtering parameters) and instead focuses on what the tool provides (exchange rates with specific metadata). It adds semantic context about the data source and return structure that the empty schema cannot convey.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('daily exchange rates from the Central Bank of Russia'), and distinguishes it from siblings by specifying the source and data type. It explicitly mentions the source (Central Bank of Russia) and the structured return format (Katzilla envelope), which differentiates it from other economic data tools like economic__exchange-rates or economic__ecb-rates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (for Russian exchange rate data from a specific official source) and implies alternatives through sibling tool names like economic__exchange-rates or economic__ecb-rates for other currency data. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the source ('DigitalOcean Status (Public API)'), update frequency ('monthly'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation information, which enhances transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: it starts with the core purpose, adds source and update details, and concludes with output specifics. Every sentence adds essential information without redundancy, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, annotations covering key behavioral traits, and an output schema implied by the description of the return format), the description is complete. It explains what the tool does, its source, update frequency, and output structure, providing all necessary context for correct invocation without overloading with extraneous details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% description coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's behavior and output. This meets the baseline for tools with no parameters, as it avoids unnecessary details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Get the current status of DigitalOcean services.' It specifies the verb ('Get'), resource ('current status of DigitalOcean services'), and distinguishes itself from siblings by focusing on a specific provider's status, unlike other tools that handle different domains like agriculture, consumer, or crypto data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to retrieve the current status of DigitalOcean services, sourced from a public API with monthly updates. However, it does not explicitly mention when not to use it or name specific alternatives among siblings, such as other status-checking tools, though the sibling list shows no direct equivalents.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond this by specifying the return format ('Katzilla envelope { data, quality, citation }') and explaining quality scores ('freshness/uptime/confidence') and citation details ('source URL, license, SHA-256 data hash'), which helps the agent understand output structure and data integrity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured, with two sentences that efficiently convey purpose, source, update frequency, and return format without any wasted words. It is front-loaded with the core function and follows with detailed output explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, rich annotations covering safety and idempotency, and an output schema (implied by 'Has output schema: true'), the description is complete. It adds necessary context about the data source, real-time updates, and detailed return format, compensating for any gaps and ensuring the agent can use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics, explaining the return structure and components like quality and citation. This adds meaningful context beyond the schema, though it's not directly about input parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get available weather radar map timestamps') and identifies the resource ('from RainViewer'). It distinguishes itself from sibling tools by specifying it's for international weather radar data, unlike other tools focused on agriculture, consumer data, etc., making its scope explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning the source ('RainViewer (Free API)') and that it updates 'real-time', implying it's for current weather radar data. However, it does not explicitly state when not to use it or name alternatives, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this by specifying the data source, update frequency, and return format details (Katzilla envelope with quality scores and citation info), which aids in understanding behavior without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source and output details in a single, efficient sentence. Every part adds value without redundancy, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, annotations provided, output schema exists), the description is complete. It explains what the tool does, the source, update frequency, and return format, which suffices for an agent to use it correctly without needing additional details from structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on output and context, which aligns with the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('List') and resource ('spacecrafts launched by the Indian Space Research Organisation (ISRO)'), making the purpose specific and clear. It distinguishes this tool from siblings by focusing on ISRO spacecraft launches, unlike other science tools like arXiv or NASA-related ones.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by specifying the source ('ISRO API (Public)') and update frequency ('updates daily'), which helps determine when to use it. However, it does not explicitly state when not to use it or name alternatives among siblings, such as other space-related tools like 'space__launch-schedule'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the source (CISA, Public Domain), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation data including SHA-256 hash). This enriches the agent's understanding of data freshness, auditability, and structure, though it doesn't mention rate limits or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by essential details about the source, update frequency, and return format. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension by an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no input parameters, rich annotations, and an output schema implied by the description of the Katzilla envelope), the description is complete. It covers purpose, source, update frequency, and return structure, which is sufficient for an agent to understand and invoke the tool correctly without needing additional explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters (0 parameters, 100% coverage), so there are no parameters to document. The description appropriately does not discuss parameters, focusing instead on output behavior. A baseline of 4 is applied as it compensates for the lack of parameters by detailing the return format, which is helpful for the agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieve the CISA Known Exploited Vulnerabilities (KEV) catalog. Returns the full list of actively exploited vulnerabilities.' It specifies the exact resource (CISA KEV catalog) and verb (retrieve), distinguishing it from sibling tools like security__nvd or security__ioda, which focus on different security data sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it's for retrieving the full list of actively exploited vulnerabilities from CISA, updated daily. However, it does not explicitly state when not to use this tool or name specific alternatives among siblings (e.g., security__nvd for broader vulnerability data), which prevents a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the data source (RIPE NCC), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details), which helps the agent understand data freshness and auditability without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by source details and return format explanation in a single, efficient sentence. Every element (purpose, source, update frequency, return structure) serves a clear purpose without redundancy, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (security data with daily updates), rich annotations (read-only, idempotent, open-world), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return format details, providing all necessary context for an agent to use the tool effectively without needing to explain parameters or basic behavioral traits already in annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description compensates by explaining the return structure ('Returns the Katzilla envelope { data, quality, citation }') and what the quality scores represent ('freshness/uptime/confidence'), adding semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Get the current list of RIPE Routing Information Service (RIS) BGP peers and their status.' It specifies the verb ('Get'), resource ('list of RIPE RIS BGP peers and their status'), and distinguishes it from siblings by focusing on security-related BGP data, unlike other tools in categories like agriculture, consumer, or entertainment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Source: RIPE NCC (Open Data), updates daily.' This indicates when the data is relevant (daily updates) and its open-source nature. However, it does not explicitly state when not to use this tool or name alternatives among siblings, such as other security tools like 'security__cisa-kev' or 'security__nvd'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the data source (BC Ferries API), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation info). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description complements these by explaining freshness and auditability without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: it front-loads the core purpose, adds source and update details, and explains the return format in a single, efficient sentence. Every part adds value without redundancy, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, annotations covering safety and idempotency, and an output schema implied by the return format description), the description is complete. It covers purpose, source, update frequency, and output structure, leaving no gaps for an agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, with 100% schema description coverage. The description does not need to explain parameters, but it implicitly confirms there are no required inputs by focusing on data retrieval without mentioning any filters or options. This is sufficient, though a perfect score is reserved for cases where parameter semantics are explicitly addressed when relevant.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get current BC Ferries schedule and capacity data') and resources ('for British Columbia ferry routes'), distinguishing it from sibling tools like transport__bts-stats or transport__uk-transport by focusing on BC Ferries specifically. It explicitly mentions the data source (BC Ferries API) and update frequency (daily), which further clarifies its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for accessing current schedule and capacity data from BC Ferries, sourced from a public API with daily updates. However, it does not explicitly state when not to use it or name alternatives among sibling tools (e.g., for historical data or other transport modes), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the external source ('is.gd'), its free nature, daily update frequency, and the specific return format ('Katzilla envelope' with quality scores and citation details), enhancing transparency without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details (source, updates, return format). Every sentence adds value: the second explains the service, and the third clarifies the output structure. It's efficiently structured with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, simple operation), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adequately explains the tool's purpose, source, update frequency, and return format, leaving no gaps for an agent to understand its use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter ('url') fully documented in the schema. The description mentions 'long URL' but doesn't add significant semantic details beyond what the schema provides (e.g., no constraints on URL length or format). This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Shorten a long URL') and resource ('using the is.gd URL shortening service'), with explicit differentiation from siblings like 'utilities__fetch-url' (which fetches URLs) and 'utilities__qr-code' (which generates QR codes). It avoids tautology by elaborating beyond the tool name/title.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage context: it specifies the service provider ('is.gd'), notes it's 'Free to use', and indicates 'updates daily' for freshness. It implicitly distinguishes from siblings by focusing on URL shortening, though no explicit 'when-not' or alternative tool is named, but the context is sufficiently clear for this standalone utility.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it discloses the source (GovInfo/GPO), update frequency (daily), return format (Katzilla envelope with data/quality/citation), and specific quality metrics (freshness/uptime/confidence). This significantly enhances behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first covers purpose, scope, formats, and PDF alternative; the second explains return format and quality metrics. Every element serves a clear purpose with zero redundancy, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given comprehensive annotations (readOnly, non-destructive, idempotent, openWorld), 100% schema coverage, and an output schema (implied by return format description), the description provides complete context. It covers source attribution, update frequency, format purposes, PDF alternative, and return structure - addressing all necessary dimensions for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds meaningful context: it explains that 'packageId' fetches full packages while 'granuleId' fetches sub-documents, clarifies format purposes ('htm for reading', XML variants for metadata), and reinforces the PDF limitation mentioned in guidelines. This provides semantic understanding beyond the schema's technical specifications.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Fetch the actual text content'), resource ('GovInfo package or granule'), and scope ('court opinion, public law, Federal Register notice, CFR section, etc.') with explicit format options. It distinguishes from sibling tools by specifying that PDFs require using 'govinfo-package' instead, showing clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool (for HTML/XML/MODS/PREMIS content) and when not to use it (for PDFs, directing to 'govinfo-package'). It names the specific alternative tool for PDF handling, creating clear decision boundaries for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it discloses the data source ('WHO GHO'), update frequency ('updates daily'), licensing ('CC-BY-NC-SA 3.0 IGO'), and return format ('Katzilla envelope { data, quality, citation }'). While annotations cover read-only, non-destructive, idempotent, and open-world hints, the description provides practical implementation details that help the agent understand what to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: purpose statement, parameter guidance, and return format details. Every sentence adds essential information with zero waste. It's front-loaded with the core functionality and progressively adds important details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, comprehensive annotations, complete schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, usage, behavioral traits, data source, licensing, update frequency, and return format - everything needed for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description enhances this by explaining the parameter's dual purpose: 'List all indicators or fetch data for a specific indicator code' and providing an example code ('WHOSIS_000001'). It adds semantic meaning about how the parameter value affects tool behavior beyond the schema's technical description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Query', 'List', 'fetch') and resources ('WHO Global Health Observatory (GHO) OData API', 'indicators', 'data for a specific indicator code'). It distinguishes from siblings by specifying it's for health/medical data from WHO, unlike other health tools like CDC or NIH sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'List all indicators or fetch data for a specific indicator code' and specifies the parameter behavior ('Use 'Indicator' to list all available indicators'). It clearly indicates when to use which mode based on the indicator parameter value.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: it details security restrictions (blocks private addresses), performance limits (5 MB cap, 5 redirects), data processing (HTML to plain text), and output format (Katzilla envelope with quality/citation details). Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description enriches this with operational specifics without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with core functionality, followed by behavioral details and usage intent, all in two dense but efficient sentences with zero waste—every phrase adds necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (fetching URLs with security and processing), rich annotations, 100% schema coverage, and an output schema (implied by describing the return format), the description is complete: it covers purpose, usage, behavior, and output without needing to repeat structured data, making it fully adequate for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3, but the description adds value by clarifying parameter semantics: it implies the 'url' parameter must be public and not blocked, and it explains the 'format' parameter's effect ('converts HTML to plain text' vs. 'raw'), though it doesn't detail 'timeoutMs' beyond the schema. This elevates it above the minimum.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('fetch', 'return') and resources ('public http(s) URL', 'body as text'), and distinguishes it from siblings by emphasizing its general-purpose page reader role for AI agents, unlike the other utilities tools (e.g., qr-code, url-shortener) which serve different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use ('Intended as a general-purpose page reader for AI agents') and provides clear exclusions ('Blocks private/loopback/link-local/metadata addresses'), with implied alternatives for non-public URLs or other utilities tools, though no specific sibling tool is named for direct comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![katzilla MCP server](https://glama.ai/mcp/servers/codeislaw101/katzilla/badges/card.svg)](https://glama.ai/mcp/servers/codeislaw101/katzilla)

Score Badge

Copy to your README.md:

[![katzilla MCP server](https://glama.ai/mcp/servers/codeislaw101/katzilla/badges/score.svg)](https://glama.ai/mcp/servers/codeislaw101/katzilla)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/codeislaw101/katzilla'

If you have feedback or need assistance with the MCP directory API, please join our Discord server