mcp-swiss

Server Quality Checklist

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

Latest release: v0.7.1
Disambiguation4/5
Most tools have clearly distinct purposes targeting specific Swiss domains (e.g., transport, weather, politics, energy), with good descriptive differentiation. However, some potential overlap exists between similar 'get' and 'search' tools (e.g., get_municipality vs search_places) that could cause minor confusion, though descriptions help clarify their distinct focuses.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, primarily using 'get_', 'list_', 'search_', and 'compare_' prefixes. This predictable structure makes it easy for agents to understand tool purposes and navigate the extensive set without naming conflicts or mixed conventions.
Tool Count2/5
With 76 tools, the count is excessive for a single server, creating cognitive overload and likely overlapping functionality. While covering many Swiss domains, this volume feels heavy and could be better organized into multiple focused servers rather than one monolithic collection.
Completeness5/5
The tool set provides remarkably comprehensive coverage across Swiss data domains, including transport, weather, politics, energy, geography, and more. Each domain appears to have complete CRUD-like operations where applicable (e.g., get/list/search variants), with no obvious gaps in the surface for the broad Swiss data aggregation purpose.
Average 3.6/5 across 76 of 76 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 2 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 15, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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.
This repository includes a glama.json configuration file.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists members with filtering, but doesn't mention whether it's a read-only operation, potential rate limits, authentication needs, pagination behavior, or what the output format looks like. For a tool with 4 parameters and no output schema, this is a significant gap in transparency.
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 a single, efficient sentence that states the core purpose upfront. It wastes no words and is appropriately sized for the tool's complexity. However, it could be slightly more structured by separating filtering details into a second sentence for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output contains (e.g., member details, pagination info) or behavioral aspects like error handling. For a data retrieval tool with filtering, more context is needed to guide 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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning filtering by canton or party, but doesn't provide additional context beyond what's 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.
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: 'List current or past Swiss Parliament members (National Council and Council of States).' It specifies the resource (Parliament members) and scope (Swiss, both chambers), and includes the verb 'List' with filtering capabilities. However, it doesn't explicitly differentiate from sibling tools like 'get_politician_interests' or 'get_parliament_votes', which reduces the score from a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions filtering options but doesn't specify prerequisites, exclusions, or compare it to related tools such as 'get_politician_interests' or 'search_parliament_business'. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what data is returned (session names, dates, types) but doesn't mention important behavioral aspects like whether this is a read-only operation, how 'upcoming and recent' is defined, time ranges, sorting order, pagination, or error conditions. For a data retrieval tool with no annotations, this leaves significant gaps.
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 a single sentence that states the core purpose and what data is returned. There's no wasted verbiage or redundancy. However, it could be slightly more front-loaded by specifying the exact scope ('Swiss parliament sessions') earlier in the sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read operation with one well-documented parameter and no output schema, the description provides the basic purpose and return data. However, it lacks important context about behavioral aspects (time ranges, sorting, error handling) and doesn't explain the relationship to other parliament tools. Without annotations or output schema, the description should provide more complete operational 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 description adds no parameter information beyond what's already in the schema (which has 100% coverage). The schema fully documents the single 'limit' parameter with its type, description, default value, and maximum. The description doesn't provide additional context about parameter usage or semantics, 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.
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 upcoming and recent Swiss parliament sessions' with specific resources (session names, dates, types). It distinguishes from siblings by focusing on parliament sessions rather than other Swiss data like weather, traffic, or companies. However, it doesn't explicitly differentiate from 'get_committee_meetings' or 'get_parliamentary_documents' which might be related parliament tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of when this tool is appropriate versus other parliament-related tools like 'get_parliamentary_documents' or 'get_parliament_members', nor any prerequisites or constraints beyond what's in the parameter description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what the tool returns ('nearby stations with traffic volume data'), it lacks critical behavioral details: whether this is a read-only operation, what format the data comes in, if there are rate limits, authentication requirements, or how 'nearby' is calculated beyond the radius parameter. For a tool with no annotation coverage, this is insufficient.
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 concise with two clear sentences that efficiently convey the core functionality. The first sentence explains what the tool does, and the second describes what it returns. There's no wasted language or redundancy. However, it could be slightly more structured by explicitly separating functionality from output description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (geographic search with traffic data), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and return type but lacks important contextual information: data format, pagination, error conditions, or how 'traffic volume data' is structured. The absence of output schema means the description should ideally provide more detail about 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%, so the schema already documents all three parameters (lat, lon, radius) with clear descriptions and examples. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain coordinate systems, valid ranges, or how radius interacts with results. 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.
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: 'Find ASTRA traffic counting stations near a geographic coordinate in Switzerland. Returns nearby stations with traffic volume data.' It specifies the verb ('Find'), resource ('ASTRA traffic counting stations'), and scope ('near a geographic coordinate in Switzerland'). However, it doesn't explicitly differentiate from sibling tools like 'get_traffic_by_canton' or 'get_traffic_count', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_nearby_stations' (which might return different station types) or 'get_traffic_count' (which might provide traffic data without geographic filtering). There's also no information about prerequisites, constraints, or typical use cases beyond the basic functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates the tool retrieves data ('Get detailed breakdown'), suggesting a read-only operation, but does not specify permissions, rate limits, error handling, or the format of the returned breakdown. This leaves significant gaps for a tool with no annotation coverage.
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 a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words, though it could be slightly more structured by separating scope details. Overall, it earns its place without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (retrieving detailed vote data), no annotations, and no output schema, the description is minimally adequate. It specifies the resource and scope but lacks details on behavioral traits, output format, or usage context, leaving room for improvement in completeness.
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 ('vote_title' and 'date'). The description adds no additional semantic information about these parameters beyond what the schema provides, such as examples or constraints, so it meets the baseline score of 3.
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 with a specific verb ('Get detailed breakdown') and resource ('a specific Swiss popular vote'), including the scope of data returned ('per-district results for Basel-Stadt'). However, it does not explicitly differentiate from the sibling tool 'get_voting_results' or 'search_votes', which might offer similar or overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_voting_results' or 'search_votes', nor does it mention any prerequisites or exclusions. It implies usage for retrieving detailed vote data but lacks explicit contextual boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves historical data, implying a read-only operation, but doesn't cover important aspects like rate limits, authentication needs, data format, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its 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 a single, clear sentence with no wasted words. It's front-loaded with the core purpose and efficiently conveys the essential information without unnecessary details, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 required parameters, no annotations, no output schema), the description is insufficient. It doesn't explain what data is returned (e.g., temperature, precipitation), how results are formatted, or any limitations (e.g., date range constraints). For a data retrieval tool with no structured output documentation, more detail is needed to be complete.
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 adds no parameter-specific information beyond what's in the input schema, which has 100% coverage. It mentions 'Swiss station' and 'historical weather data', which loosely relates to the 'station' parameter and date ranges, but doesn't provide additional context like station code examples or date 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.
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 historical weather data for a Swiss station'. It specifies the verb ('Get'), resource ('historical weather data'), and geographic scope ('Swiss station'), making it easy to understand. However, it doesn't differentiate from sibling tools like 'get_weather' or 'list_weather_stations', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_weather' (likely for current weather) or 'list_weather_stations', nor does it specify prerequisites or exclusions. The user must infer usage from the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'identifies' features and layers, which suggests a read-only operation, but doesn't clarify permissions, rate limits, data sources, or response format. For a tool with no annotations and no output schema, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates what the tool does, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'identify' entails (e.g., returns a list, details format), data sources, or error handling. For a tool with 3 parameters and no structured behavioral hints, more context is needed to guide 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 input schema already documents all parameters (lat, lng, layers) with clear descriptions. The description adds no additional parameter semantics beyond implying Swiss location scope. This meets the baseline of 3, 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.
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: 'Identify geographic features and data layers at a specific Swiss location.' It specifies the verb ('identify'), resource ('geographic features and data layers'), and scope ('Swiss location'). However, it doesn't explicitly differentiate from sibling tools like 'geocode' or 'reverse_geocode' that might also handle location data, though the focus on features/layers is distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., coordinate format), exclusions, or comparisons to siblings like 'get_weather' or 'get_snow_conditions' that might provide related geographic data. Usage is implied only by the tool's name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions the data source (OpenParlData) but doesn't cover permissions, rate limits, pagination, error handling, or what the search returns (e.g., list of affairs with details). For a search tool with zero annotation coverage, this is inadequate.
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 a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 26 cantons), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what constitutes a 'political affair', the format of results, or any limitations (e.g., date ranges, availability). This leaves significant gaps 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?
Schema description coverage is 100%, so the schema fully documents parameters. The description adds no parameter-specific information beyond implying a search across cantons, which is already clear from the schema's canton field. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches political affairs across Swiss cantonal parliaments, specifying the scope (all 26 cantons) and data source (OpenParlData). It distinguishes from siblings by focusing on cantonal affairs rather than federal or other domains, though it doesn't explicitly contrast with similar tools like search_parliament_business.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, typical use cases, or how it differs from sibling tools like search_parliament_business or search_votes, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the data source (ZEFIX) but doesn't describe response format, pagination behavior, rate limits, authentication requirements, or error conditions. For a search tool with no structured safety hints, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the essential information without waste. It's appropriately sized for a search tool and front-loads the key action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the search returns, how results are structured, or any limitations. The agent must rely entirely on the input schema without understanding the tool's behavior or output.
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 thoroughly. The description adds minimal value by listing the searchable fields ('by name, canton, or legal form') but doesn't provide additional context beyond what's in the parameter descriptions.
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 action ('Search') and resource ('Swiss company registry (ZEFIX)'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'search_companies_by_address', which appears to be a related but distinct search method.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_companies_by_address' or 'get_company'. It mentions searchable fields but offers no context about typical use cases, prerequisites, or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the search scope but fails to describe critical traits like whether this is a read-only operation, any rate limits, authentication needs, or what the output format might be (e.g., list of results with details). For a search tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a search tool. It doesn't explain what the tool returns (e.g., structured data, error handling), behavioral constraints, or how it differs from siblings. This leaves the agent with insufficient context to use it effectively beyond basic parameter input.
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 'type' parameters. The description adds no additional semantic details beyond what the schema provides, such as examples of valid 'type' values or search syntax. 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.
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 as searching for Swiss place names, localities, mountains, and geographic features, which is a specific verb ('search') with a well-defined resource scope. However, it does not explicitly differentiate from sibling tools like 'geocode' or 'identify_location', which might have overlapping geographic functions, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as 'geocode' for coordinate-based lookups or 'identify_location' for reverse geocoding. It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return format (titles, IDs, descriptions) which is helpful, but doesn't cover important behavioral aspects like rate limits, authentication requirements, error conditions, pagination behavior, or whether this is a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps.
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 a single, efficient sentence that communicates the core purpose and return format. It's appropriately sized for a search tool and front-loads the essential information. There's no wasted verbiage, though it could potentially benefit from additional context about when to use it.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 2 parameters, 100% schema coverage, and no output schema, the description provides basic but incomplete context. It covers what the tool does and what it returns, but lacks behavioral details (especially important with no annotations) and usage guidance. The absence of an output schema means the description should ideally provide more detail about return values, though it does mention the key fields returned.
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 both parameters (query and limit). The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
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 searches Swiss Federal Statistical Office datasets on opendata.swiss and returns matching dataset titles, IDs, and descriptions. It specifies the verb 'search' and resource 'datasets', but doesn't explicitly differentiate from sibling tools like 'search_cantonal_affairs' or 'search_companies' that also search different 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is appropriate compared to other search tools on the server (like search_companies or search_swiss_news) or when not to use it. The only implied usage is for statistical data, but this isn't explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool searches and returns matching articles but lacks critical details: authentication requirements, rate limits, error conditions, pagination behavior, or what fields are returned. For a search tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 concise—two sentences that directly state the tool's function and scope. It's front-loaded with the core purpose and avoids unnecessary elaboration. However, it could be slightly more structured by explicitly separating scope from behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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), no annotations, and no output schema, the description is minimally adequate. It covers what the tool does but lacks details about return format, error handling, and operational constraints. For a search tool without output schema, more information about result structure would be beneficial.
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 fully documents both parameters (query and limit). The description adds minimal value beyond the schema—it mentions 'keyword or phrase' which aligns with the schema's description, but doesn't provide additional context about search syntax, language support, or result ordering. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches Swiss news headlines from SRF by keyword, specifying both the action (search) and resource (Swiss news headlines). It distinguishes itself from the sibling 'get_swiss_news' by focusing on keyword-based search rather than general retrieval. However, it doesn't explicitly contrast with other search tools like 'search_cantonal_affairs' or 'search_parliament_business'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions searching across all available news categories but doesn't specify when to use this tool versus alternatives like 'get_swiss_news' (which appears to retrieve news without search) or other search tools for different data types. No exclusions, prerequisites, or comparative context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the search scope (vote titles) and return format (matching votes with yes/no results), but lacks critical behavioral details such as language support (though implied by German/French/Italian examples), result ordering, pagination, error handling, or whether this is a read-only operation. The description provides basic functionality but misses important operational context.
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 concise - a single sentence that efficiently communicates the core functionality. It's front-loaded with the main purpose and includes helpful examples. There's no wasted text, though it could potentially benefit from slightly more structure for complex aspects.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 functionality with 2 parameters), no annotations, and no output schema, the description provides adequate but incomplete coverage. It explains what the tool does and what it returns, but lacks details about behavioral characteristics, error conditions, and how results are structured. For a search tool without output schema, more detail about return format would be beneficial.
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 both parameters. The description adds marginal value by providing keyword examples ('Initiative', 'Klimaschutz', 'CO2', 'AHV') that illustrate potential query values, but doesn't add significant semantic context beyond what's in the schema descriptions. 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.
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: searching Swiss popular votes by keyword in vote titles and returning matching votes with yes/no results. It specifies the resource (Swiss popular votes) and action (search by keyword), but doesn't explicitly differentiate from sibling tools like 'get_vote_details' or 'get_voting_results' beyond the search functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions searching by keyword with examples ('Initiative', 'Klimaschutz'), but offers no explicit guidance on when to use this tool versus alternatives like 'get_vote_details' or 'get_voting_results'. There's no mention of prerequisites, limitations, or comparative use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data source ('swisstopo') but lacks details on rate limits, error handling, authentication needs, or output format. For a geocoding tool, this omission is significant as users need to understand reliability and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It front-loads the core functionality and includes the data source, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (geocoding with a specific regional focus), no annotations, and no output schema, the description is incomplete. It covers the basic purpose but lacks behavioral details, usage context, and output information, which are crucial for effective tool 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?
The schema description coverage is 100%, with the single parameter 'address' well-documented in the schema. The description adds minimal value beyond the schema by specifying 'Swiss address or place name', but doesn't provide additional context like format examples or validation rules. 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.
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: converting Swiss addresses/place names to coordinates using swisstopo. It specifies the verb ('Convert'), resource ('Swiss address or place name'), and target ('coordinates'), distinguishing it from sibling tools like 'reverse_geocode' or 'identify_location'. However, it doesn't explicitly differentiate from 'search_places', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'reverse_geocode' (for the inverse operation) or 'search_places' (which might also handle location queries), nor does it specify prerequisites or constraints beyond the Swiss address scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'live arrivals' which implies real-time data, but doesn't disclose rate limits, data freshness, error conditions, authentication needs, or what the response format looks like. This leaves significant gaps for an agent to understand operational 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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple query tool and front-loads the essential information about what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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, no output schema, no annotations), the description is minimally adequate. It states the core purpose but lacks behavioral context, usage guidance, and output information. The schema handles parameter documentation well, but the overall picture remains incomplete for an agent to use the tool confidently.
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 documentation for all three parameters. The description adds no additional parameter 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.
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 live arrivals at a Swiss transport station.' It specifies the verb ('Get'), resource ('arrivals'), and geographic scope ('Swiss transport station'). However, it doesn't explicitly differentiate from its sibling 'get_departures' or other transport-related tools, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_departures' or 'get_connections' for related transport queries, nor does it specify prerequisites or contextual constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Get live departures' implies a read-only operation, it doesn't specify whether this requires authentication, has rate limits, what format the data returns, or whether it's real-time versus cached data. The description adds minimal behavioral context beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states exactly what the tool does without unnecessary words. It's appropriately sized for a straightforward data retrieval tool and gets directly to the point with zero wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only data retrieval tool with good schema coverage but no output schema, the description provides basic context but lacks important details. Without annotations or output schema, the description should ideally mention what format the departures data returns (e.g., list of departures with times/destinations) or any notable behavioral characteristics. It's minimally adequate but has clear 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?
With 100% schema description coverage, the input schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.
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 action ('Get live departures') and the resource ('from a Swiss transport station'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_arrivals' or 'get_connections' which likely serve related but distinct purposes in the transport domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'get_arrivals', 'get_connections', and 'get_nearby_stations' available, there's no indication of how this tool differs or when it's the appropriate choice. The description only states what it does, not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] information,' implying a read-only operation, but doesn't specify what type of information is returned (e.g., demographic data, location details), whether there are rate limits, authentication needs, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its 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 a single, clear sentence with no wasted words: 'Get information about a Swiss municipality by name.' It is front-loaded and efficiently communicates the core purpose without unnecessary elaboration, 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.
Completeness3/5
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, 100% schema coverage, no output schema), the description is adequate but incomplete. It lacks details on the information returned, potential errors, or usage context, which are important for an agent to invoke it correctly. Without annotations or an output schema, the description should do more to compensate, but it meets a minimal viable standard.
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' documented as 'Municipality name.' The description adds minimal value beyond this, only reinforcing that input is 'by name.' Since the schema already fully describes the parameter, the baseline score of 3 is appropriate, as the description doesn't provide additional syntax, format details, or examples.
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 information about a Swiss municipality by name.' It specifies the verb ('Get'), resource ('Swiss municipality'), and key input ('by name'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'search_municipality_energy' or 'list_cantons,' which slightly limits its clarity in a crowded toolset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons to sibling tools such as 'geocode,' 'identify_location,' or 'search_places,' which might offer overlapping functionality. This lack of context leaves the agent to infer usage based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it indicates this is a query/find operation (implied read-only), it doesn't mention important behavioral aspects like rate limits, authentication requirements, response format, error conditions, or whether results are sorted by distance. For a location-based query tool with no annotation coverage, this represents significant gaps in behavioral 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 perfectly concise at just 7 words: 'Find Swiss public transport stations near given coordinates'. Every word earns its place - it specifies the action, resource type, geographic scope, and location input format. There's zero waste or redundancy, and the meaning is front-loaded in a single efficient sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (location-based query with 4 parameters) and the absence of both annotations and output schema, the description is minimally adequate but incomplete. It tells what the tool does but lacks crucial context about return values (no output schema), behavioral constraints, and differentiation from similar tools. The description would need to compensate more for the missing structured information to achieve a higher score.
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 four parameters thoroughly (x as longitude, y as latitude, limit with default, distance in meters). The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain coordinate systems, distance calculation methods, or result ordering. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
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: 'Find Swiss public transport stations near given coordinates'. It specifies the verb ('Find'), resource ('Swiss public transport stations'), and scope ('near given coordinates'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'search_stations' or 'get_traffic_nearby', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available (including 'search_stations' and 'get_traffic_nearby'), there's no indication of when this specific nearby-station-finding tool is appropriate versus other station-related or location-based tools. The description simply states what it does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool retrieves but lacks critical behavioral details: whether this is a read-only operation, if there are rate limits, authentication requirements, pagination behavior, or what happens when no documents exist. The description is minimal and doesn't compensate for missing 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 concise with a single sentence that efficiently communicates the core purpose. It's front-loaded with the main action and includes specific document types without unnecessary elaboration. However, it could be slightly more structured by separating purpose from parameter context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what format documents are returned in, whether they include metadata, how errors are handled, or any limitations beyond the implied limit parameter. The description leaves too many behavioral questions unanswered given the lack of 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?
Schema description coverage is 100%, providing good documentation for both parameters. The description adds no additional parameter semantics beyond what the schema already explains about affair_id and limit. This meets the baseline score of 3 when schema coverage is high, but doesn't enhance understanding.
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 with a specific verb ('Get') and resource ('official documents for a parliamentary affair'), including document types like reports and committee opinions. It distinguishes from most siblings by focusing on parliamentary documents, though it doesn't explicitly differentiate from similar tools like get_parliament_votes or get_parliament_members.
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 needing parliamentary documents, but provides no explicit guidance on when to use this tool versus alternatives like get_parliament_votes or search_parliament_business. It mentions the affair_id parameter comes from search_parliament_business results, which offers some contextual hint but not clear usage rules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what data is retrieved but lacks details on permissions, rate limits, data freshness, or response format. For a tool with no annotations, this is a significant gap, as it doesn't inform the agent about operational constraints or expected behavior beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose with specific examples. There is no wasted language, and it directly communicates the tool's function without unnecessary elaboration, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (retrieving structured data for a specific entity), no annotations, and no output schema, the description is minimally adequate. It states what data is fetched but lacks details on output format, error handling, or dependencies. The high schema coverage helps, but more behavioral context would improve completeness for a tool with no 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?
The description does not mention parameters, but the input schema has 100% description coverage, clearly documenting the single required 'person_id' parameter. This meets the baseline of 3, as the schema adequately explains the parameter, and the description doesn't need to add redundant information. However, it doesn't provide additional 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 declared interests and mandates of a Swiss parliament member — board memberships, consulting roles, organizations.' It specifies the verb ('Get'), resource ('declared interests and mandates'), and target ('Swiss parliament member'), with concrete examples. However, it does not explicitly differentiate from sibling tools like 'get_parliament_members' or 'search_parliament_business', which could provide related data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the need for a 'person_id' from 'get_parliament_members results' in the schema, but this is not stated in the description itself. There is no mention of prerequisites, context, or exclusions, leaving the agent to infer usage from parameter hints alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but does not specify behavioral traits such as rate limits, authentication requirements, data freshness, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does and its scope ('in Switzerland'), with zero waste or redundancy. This is appropriately sized for a simple lookup tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a location-based query with no output schema and no annotations), the description is minimally complete. It covers the purpose and scope but lacks details on behavioral aspects, output format, or usage context. Without annotations or output schema, the description should do more to compensate, but it provides a basic foundation.
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 parameters 'lat' and 'lng' clearly documented as 'Latitude (WGS84)' and 'Longitude (WGS84).' The description adds no additional parameter semantics beyond what the schema provides, such as valid ranges or units. With high schema coverage, the baseline score of 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.
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 rooftop solar energy potential for a location in Switzerland.' It specifies the verb ('Get'), resource ('rooftop solar energy potential'), and geographic scope ('Switzerland'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools, as none appear to directly overlap in function (e.g., no other solar-related tools are listed).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., location accuracy needs), exclusions (e.g., non-Switzerland locations), or comparisons to sibling tools (e.g., whether other tools like 'get_weather' might provide related data). Usage is implied only by the tool's name and description, lacking explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves historical data but doesn't describe what 'historical' entails (e.g., how far back data goes), response format (e.g., time series, units), error handling (e.g., invalid station IDs), rate limits, or authentication needs. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does ('Get historical river/lake water level data') and its scope ('for a Swiss hydrological station'), with zero waste or redundancy. This is appropriately concise for a straightforward data retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (historical data retrieval with 3 required parameters), no annotations, and no output schema, the description is minimally adequate but incomplete. It covers the basic purpose but lacks behavioral details (e.g., data format, limitations) and usage guidelines. The high schema coverage helps, but without annotations or output schema, more context on behavior and results would improve completeness.
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 (e.g., 'Hydro station ID', 'Start date YYYY-MM-DD'). The tool description adds no additional parameter semantics beyond what the schema provides—it doesn't explain station ID formats, date range constraints, or data granularity. With high schema coverage, the baseline score of 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.
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 historical river/lake water level data for a Swiss hydrological station.' It specifies the verb ('Get'), resource ('historical river/lake water level data'), and geographic scope ('Swiss hydrological station'). However, it doesn't explicitly differentiate from sibling tools like 'get_water_level' (which likely provides current data) or 'get_weather_history' (which is weather-related, not water levels).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_water_level' (likely for current water levels) or 'list_hydro_stations' (for station listings), nor does it specify prerequisites (e.g., needing a valid station ID) or contextual constraints (e.g., date range limitations). Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the data source ('swisstopo') but does not disclose behavioral traits such as rate limits, error handling, response format, or whether it's a read-only operation. The description is minimal and lacks critical operational context.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core function, though it could be slightly more structured by including usage context. No waste, but minimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what the output looks like (e.g., address format), error conditions, or limitations (e.g., only works for Swiss coordinates). For a tool with no structured output documentation, this leaves 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 both parameters ('lat' and 'lng') well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as coordinate ranges or precision. 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 a specific verb ('Convert') and resource ('coordinates to a Swiss address'), and it distinguishes itself from sibling tools by specifying the geographic scope ('Swiss') and data source ('swisstopo'). This is more specific than the generic 'geocode' sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention the sibling 'geocode' tool or specify that this is for Swiss addresses only, which might be implied but not explicitly stated. No prerequisites 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the search action but doesn't disclose behavioral traits like rate limits, authentication needs, response format, pagination, or whether it's read-only. For a search tool with no annotations, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple search tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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), no annotations, and no output schema, the description is incomplete. It covers the basic purpose but lacks details on behavior, output format, and usage context, which are needed for effective agent operation.
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 both parameters ('address' and 'limit'). The description adds minimal value beyond the schema by implying the address parameter is for searching, but doesn't provide additional context like format examples or search behavior details.
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 verb 'search' and resource 'Swiss companies', with the specific scope 'registered at a specific address or locality'. It distinguishes from the generic sibling 'search_companies' by specifying address-based filtering, though it doesn't explicitly mention that distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'search_companies' or 'get_company'. The description implies usage for address-based queries but doesn't mention prerequisites, exclusions, or specific scenarios where this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool lists cantons with codes, but doesn't disclose behavioral traits such as whether it's a read-only operation, if there are rate limits, how data is formatted, or if it requires authentication. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key information ('List all Swiss cantons with their codes'). There is no wasted text, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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, no output schema, no annotations), the description is adequate but has gaps. It explains what data is returned, but without annotations or output schema, it lacks details on behavior, format, or constraints. For a basic list tool, it meets minimum viability but could be more complete.
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 adds value by specifying what data is returned ('Swiss cantons with their codes'), which compensates for the lack of an output schema. This provides clear semantics beyond the empty 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 verb ('List') and resource ('Swiss cantons'), making the purpose evident. It distinguishes from siblings by focusing on cantons rather than other Swiss data like municipalities or postcodes. However, it doesn't explicitly differentiate from 'list_postcodes_in_canton' or 'search_cantonal_affairs', which are related but distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_dams_by_canton' or 'list_postcodes_in_canton'. The description implies usage for retrieving canton data, but it lacks explicit context, prerequisites, or exclusions, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool lists legal forms but doesn't disclose behavioral traits such as whether it's a read-only operation, potential rate limits, authentication needs, or the format/scope of the returned list (e.g., completeness, ordering).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with no wasted words. It is front-loaded and appropriately sized for a simple listing tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no annotations and no output schema, the description is minimally adequate but lacks completeness. It doesn't explain the return format (e.g., list structure, fields) or behavioral context, which could help an agent use it correctly despite the simplicity.
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 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately focuses on the tool's purpose without redundant parameter details, aligning with the baseline for zero-parameter tools.
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 verb ('List') and resource ('Swiss company legal forms'), with specific examples (AG, GmbH) that clarify the domain. It distinguishes itself from siblings by focusing on legal forms rather than other Swiss data types, though it doesn't explicitly contrast with any specific sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. While the description implies it's for retrieving Swiss legal forms, it doesn't mention prerequisites, related tools (e.g., get_company for company details), or constraints like data freshness.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions listing 'all available' stations but does not specify details like pagination, rate limits, data freshness, or output format. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, clearly front-loading the purpose. It is appropriately sized for a simple list operation with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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, no output schema, no annotations), the description is minimally adequate but lacks details on output behavior (e.g., format, scope of 'all available'). It meets basic needs but could be more informative 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.
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, meaning no parameters are documented in the schema. The description does not add parameter information, which is acceptable since there are no parameters to explain. A baseline of 4 is appropriate for zero-parameter tools.
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 action ('List') and resource ('all available MeteoSwiss weather stations in Switzerland'), providing a specific verb+resource combination. However, it does not distinguish itself from sibling tools like 'list_air_quality_stations' or 'list_snow_stations' beyond specifying the type of stations, which is a minor gap in differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives, such as 'get_nearby_stations' or 'search_stations', nor does it mention any prerequisites or exclusions. It simply states what the tool does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool 'Returns speaker info and speech details', which adds some behavioral context about output content. However, it doesn't disclose critical traits like whether this is a read-only operation, rate limits, authentication needs, pagination behavior (beyond the limit parameter), or error handling. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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 concise with two sentences that directly state the purpose and return values. It's front-loaded with the core function and avoids unnecessary words. However, it could be slightly more structured by explicitly separating purpose from output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with full schema coverage but no annotations and no output schema, the description provides basic purpose and output content but lacks completeness. It doesn't explain the return format structure, error conditions, or behavioral constraints needed for a tool that fetches parliamentary data. The context is adequate for simple use but incomplete for robust agent operation.
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 well-documented in the schema (affair_id as 'OpenParlData affair ID' with source hint, limit with default and max values). The description doesn't add any parameter semantics beyond what the schema already provides, 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.
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 debate speeches and contributions for a specific parliamentary affair' with the verb 'get' and resource 'debate speeches and contributions'. It distinguishes from siblings like 'search_parliament_business' by focusing on speeches rather than business listings, but doesn't explicitly differentiate from 'get_parliamentary_documents' or 'get_parliament_votes' which might overlap in parliamentary 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 'for a specific parliamentary affair' and the input schema hints at dependency on 'search_parliament_business results' for the affair_id. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_parliamentary_documents' or 'get_parliament_votes', and doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the search functionality but fails to describe key behaviors such as response format, pagination, rate limits, error handling, or whether it's a read-only operation. This leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core purpose and is appropriately sized for a search tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of stations with details), how results are structured, or any behavioral constraints. For a search tool with 4 parameters, this leaves the agent under-informed about critical operational aspects.
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 schema already documents all parameters. The description adds marginal value by implying that 'query' is for name-based searches and 'x'/'y' for coordinate-based searches, but it doesn't provide additional syntax, format details, or usage examples beyond what the schema specifies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 ('Swiss public transport stations/stops'), and distinguishes it from siblings by focusing on station search rather than other transport or location tools. It specifies the search criteria ('by name or coordinates'), making it highly specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_nearby_stations' or 'search_places'. It lacks explicit instructions on prerequisites, exclusions, or typical use cases, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions optional filtering but doesn't describe other behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or what the return format looks like (e.g., list of meetings with dates/times). For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that front-loads the core purpose and includes optional filtering information. There is no wasted language, and it's appropriately sized for a simple retrieval tool with two parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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, no output schema, no annotations), the description is minimally adequate. It covers the purpose and hints at filtering but lacks details on behavioral aspects (e.g., read-only nature, return format) that would be important for an agent to use it correctly. Without annotations or an output schema, the description should do more to compensate, but it only meets the bare minimum.
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%, meaning the input schema already fully documents both parameters (group_id and limit). The description adds minimal value beyond the schema by mentioning 'Optionally filter by committee group ID,' which aligns with the schema's description for group_id. It doesn't provide additional context like typical group_id values or usage examples, 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.
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 Swiss parliament committee/commission meeting schedule.' It specifies the resource (meeting schedule) and scope (Swiss parliament committees/commissions). However, it doesn't explicitly differentiate from sibling tools like 'get_session_schedule' or 'search_parliament_business,' which might also involve parliamentary scheduling or meetings.
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 implied usage guidance by mentioning 'Optionally filter by committee group ID,' suggesting this tool is for retrieving meeting schedules with optional filtering. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_session_schedule' or 'search_parliament_business,' nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool's function but doesn't describe response format, error conditions, rate limits, authentication needs, or data freshness. For a read operation with 6 parameters, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a straightforward query tool and front-loads the essential information without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only query tool with full schema coverage but no output schema, the description adequately states the purpose but lacks information about return values, error handling, or data limitations. It's minimally complete but leaves the agent guessing about the response 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%, so the schema fully documents all 6 parameters with clear descriptions and defaults. The description adds no additional parameter information beyond what's in the schema, meeting 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 ('Get') and resource ('train/bus connections between two Swiss locations'), distinguishing it from sibling tools like get_departures or get_arrivals that focus on single locations. It precisely identifies the transportation modes and geographical scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like get_departures or get_arrivals, nor any prerequisites or constraints beyond what's implied by the parameters. The description lacks explicit usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the data source (SNB) and return type ('latest monthly average rate and currency details'), but lacks details on rate limits, error handling, authentication needs, or whether it's a read-only operation. This is a significant gap for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions 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 concise, consisting of two efficient sentences that directly state the tool's function and return value. There is no wasted verbiage, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return type but lacks behavioral details like error cases or performance characteristics. Without annotations or output schema, more context would improve completeness.
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 'currency' parameter well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples 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.
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 the current CHF exchange rate for a currency from the Swiss National Bank (SNB).' It specifies the verb ('Get'), resource ('CHF exchange rate'), and data source (SNB). However, it doesn't explicitly differentiate from its sibling 'get_exchange_rate_history', which handles historical rates versus current rates.
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 'current' exchange rate and 'latest monthly average,' suggesting this is for real-time or recent data. It doesn't explicitly state when to use this versus 'get_exchange_rate_history' or other alternatives, nor does it mention prerequisites or exclusions, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return limit ('up to 20 stations') and that traffic data is included, but lacks details on permissions, rate limits, data freshness, error handling, or pagination behavior. For a data-fetching tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero waste: the first states the purpose and scope, and the second adds crucial behavioral detail (return limit and data type). It is front-loaded and efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete for a simple query tool. It covers the core purpose and a key constraint (20-station limit), but lacks details on output format, error cases, or deeper behavioral context, leaving room for improvement.
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 'canton' parameter with examples. The description adds no additional parameter semantics beyond implying canton-based filtering, which is already covered. 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 ('List'), resource ('ASTRA traffic counting stations'), and scope ('in a Swiss canton'), with additional detail about what data is returned ('traffic data'). It distinguishes itself from sibling tools like 'get_traffic_count' or 'get_traffic_nearby' by specifying canton-based filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives like 'get_traffic_nearby' or 'list_snow_stations'. The description mentions canton-based filtering but does not clarify prerequisites, exclusions, or comparative use cases with other traffic-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the data source and search scope but lacks details on behavioral traits such as rate limits, authentication needs, response format, or error handling, which are critical for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions 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, consisting of two efficient sentences that convey the tool's purpose and data source without unnecessary details or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 output schema, no annotations), the description is adequate but incomplete. It covers the purpose and source but lacks behavioral context and usage guidelines, leaving gaps for an AI agent to infer proper 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 baseline is 3. The description does not add meaning beyond the schema, which already documents 'query' and 'limit' parameters clearly with examples and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 Parliament political affairs') and resources ('bills, motions, interpellations, postulates, questions, and initiatives'), and distinguishes it from siblings by specifying the data source (OpenParlData.ch) and scope (Federal Assembly).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. While it mentions the data source, it does not specify use cases, prerequisites, or contrast with sibling tools like 'search_parliament_speeches' or 'get_parliamentary_documents'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the return format 'holiday periods (start/end dates) by canton', which adds value beyond the input schema. However, it lacks details on data sources, update frequency, error handling, or any rate limits or authentication requirements, leaving behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys purpose, parameters, and output. It is front-loaded with the main action and includes no redundant information, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 annotations, no output schema), the description covers the basics: what it does, key parameters, and return format. However, without annotations or output schema, it lacks details on behavioral aspects like data freshness or error cases, making it adequate but not fully complete.
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 both parameters thoroughly. The description adds marginal value by reinforcing the optional canton filtering and return structure, but does not provide additional syntax or format details beyond what the schema specifies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 resource 'Swiss school holidays', specifies the scope 'for a given year', and distinguishes from siblings by mentioning optional canton filtering. It directly addresses what the tool does without being tautological.
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 school holiday data is needed, and the parameter description for 'canton' mentions 'If omitted, returns school holidays for all cantons', providing some contextual guidance. However, it lacks explicit when-to-use guidance compared to alternatives like 'get_public_holidays' or 'is_holiday_today'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the data source (SRF) and return format, but lacks details on rate limits, authentication needs, error handling, or whether the operation is read-only (implied but not stated). For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions 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 and efficiently adds return details in the second. Both sentences earn their place by providing essential information without redundancy or fluff, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 output schema, no annotations), the description covers the basic purpose and return format adequately. However, it lacks details on behavioral aspects like rate limits or error handling, which would be beneficial for a tool with no annotations, making it minimally complete but with clear 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?
The input schema has 100% description coverage, clearly documenting both parameters with enums and defaults. The description does not add any parameter-specific semantics beyond what the schema provides, so it meets the baseline score 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 ('Get the latest Swiss news headlines') and resources ('from SRF'), distinguishing it from sibling tools like 'search_swiss_news' by focusing on top headlines rather than search functionality. It specifies the return format (articles with title, description, link, publication date).
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 latest headlines, but does not explicitly state when to use this tool versus alternatives like 'search_swiss_news'. No guidance on prerequisites, exclusions, or specific contexts is provided, 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool retrieves 'current' data, implying it's a read-only operation, but does not disclose behavioral traits such as rate limits, authentication needs, error handling, or data freshness. This leaves gaps for a tool that likely queries external APIs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get current river or lake water level and temperature') and specifies the context ('at a Swiss hydrological station'). There is no wasted wording, and it is appropriately sized for a simple tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 output schema, no annotations), the description is minimally complete. It covers the basic purpose but lacks details on behavioral aspects like error cases or return format, which are important for an agent to use it correctly. Without annotations or output schema, more context would be beneficial.
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 the parameter 'station' well-documented in the schema (including examples like '2135 for Aare/Bern'). The description does not add meaning beyond this, as it only reiterates the station context without providing additional syntax or format details. The baseline score of 3 is appropriate given the 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 ('Get current river or lake water level and temperature') and the resource ('at a Swiss hydrological station'), making the purpose specific and unambiguous. It distinguishes itself from siblings like 'get_water_history' (historical data) and 'list_hydro_stations' (listing stations), 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'current' data, suggesting it's for real-time queries rather than historical analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_water_history' or 'list_hydro_stations', and does not mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data source and station codes but does not describe response format, error conditions, rate limits, or whether this is a read-only operation. The description adds minimal behavioral context beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that immediately states the tool's purpose, scope, and provides helpful examples. 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with no output schema and no annotations, the description provides adequate basic context about what the tool does and parameter examples. However, it lacks information about response format, error handling, and behavioral characteristics that would be helpful for an AI 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%, so the schema already documents the 'station' parameter with examples. The description adds marginal value by reinforcing the station code concept with additional examples (BER, ZUE, LUG), but doesn't provide additional semantic context beyond what's 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 ('current weather conditions'), specifies the data source ('Swiss MeteoSwiss station'), and distinguishes it from sibling tools like 'get_weather_history' and 'list_weather_stations' by focusing on current conditions at a specific station.
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 Swiss MeteoSwiss stations and providing station code examples, but does not explicitly state when to use this tool versus alternatives like 'get_weather_history' or 'get_nearby_stations'. No explicit 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool returns ('dataset names, descriptions, and resource download URLs') which is helpful, but doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or pagination behavior for results beyond the limit parameter.
Agents need to know what a tool does to the world before calling it. Descriptions 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 structured with three concise sentences that each earn their place: first establishes the action and scope, second elaborates on the data domain, third specifies the return format. No wasted words, front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 2 parameters and no output schema, the description provides adequate but incomplete context. It covers the purpose, data domain, and return format, but lacks behavioral details (especially important with no annotations) and doesn't explain how results are structured or formatted. The absence of output schema means the description should ideally provide more detail about the 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 adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation 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 specific action ('Search opendata.swiss'), resource ('Swiss real estate and housing datasets'), and scope ('Finds datasets about property prices, rents, housing construction, vacancy rates, and more'). It effectively distinguishes this tool from its many siblings by focusing exclusively on real estate data from a specific Swiss open data portal.
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 domain (real estate/housing) and source (opendata.swiss), but provides no explicit guidance on when to use this tool versus alternatives. With many sibling tools available (like get_property_price_index, get_rent_index, search_statistics), there's no indication of how this search tool complements or differs from those specific real estate tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the API source, it doesn't disclose important behavioral traits like rate limits, authentication requirements, error handling, response format, or whether this is a read-only operation. The description is insufficient for a tool with 6 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions 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 with zero waste. The first sentence states the core purpose and data source, while the second provides usage context. Every word earns its place, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (earthquake details, counts, or raw data), doesn't mention pagination or result ordering, and provides no information about error conditions or API limitations. The description should do more to compensate for the lack of 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?
The schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description adds minimal value beyond the schema by mentioning 'Swiss city, landmark, or custom coordinates' which relates to lat/lon parameters, but doesn't provide additional semantic context about parameter interactions or usage patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 earthquakes near a geographic location'), identifies the resource ('earthquakes'), and specifies the data source ('Swiss Seismological Service (SED) FDSN API'). It distinguishes itself from sibling tools like 'get_recent_earthquakes' by emphasizing location-based filtering rather than recency.
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 ('useful for finding seismic activity near a Swiss city, landmark, or custom coordinates'), which helps differentiate it from other earthquake-related tools. However, it doesn't explicitly mention when NOT to use it or name specific alternatives like 'get_recent_earthquakes' for non-location-based searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the data source ('official BFS data'), baseline ('Q4 2019 = 100'), temporal coverage ('since 2009'), and breakdowns ('all properties, single-family houses, and apartments'), but lacks details on rate limits, error handling, or response format, which are important for a data-fetching tool.
Agents need to know what a tool does to the world before calling it. Descriptions 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 efficiently uses three sentences to cover data source, baseline, temporal scope, and breakdowns without any redundant or vague language. Every sentence adds essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data-retrieval tool with no annotations and no output schema, the description adequately covers what the tool does and the data's nature. However, it lacks details on output format (e.g., JSON structure, units) and error cases, which are important for an agent to handle responses correctly, leaving some gaps in completeness.
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 schema fully documents all three parameters. The description adds value by explaining the index's baseline and breakdowns, which contextualizes the 'type' parameter options, but does not provide additional syntax or format details beyond the schema. The baseline score of 3 is raised due to this contextual 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 specific verbs ('Get') and resources ('Swiss Residential Property Price Index'), including the data source ('official BFS data'). It distinguishes itself from siblings by focusing on property price indices, unlike other tools for weather, traffic, or parliamentary data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or compare it to similar tools (e.g., 'get_rent_index' or 'search_real_estate_data'), leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the return format ('currency codes, names, and regions'), which is helpful, but it doesn't disclose behavioral traits such as data freshness, rate limits, authentication needs, or error handling. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose and includes essential details like the source and return format. There's no wasted verbiage, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and return format, but without annotations or output schema, it should ideally include more behavioral context (e.g., data source reliability, update frequency) to be fully complete.
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 0 parameters with 100% schema description coverage, so no parameter information is needed. The description doesn't add parameter semantics, but that's acceptable here. A baseline of 4 is appropriate as it doesn't detract from 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 action ('List all currencies'), specifies the resource ('available from the Swiss National Bank (SNB) for CHF exchange rate data'), and distinguishes it from siblings like 'get_exchange_rate' or 'get_exchange_rate_history' by focusing on metadata rather than rates. 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining currency metadata before fetching exchange rates, but it doesn't explicitly state when to use this tool versus alternatives like 'get_exchange_rate' or provide exclusions. The context is clear but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states it lists stations but does not disclose behavioral traits such as whether the data is real-time or historical, if there are rate limits, authentication needs, or what the output format looks like. This leaves significant gaps for an agent to understand how to use the tool effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key information ('List all available BAFU hydrological monitoring stations') without any wasted words. It is appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no annotations, and no output schema, the description is minimal but adequate for a basic listing operation. However, it lacks details on output format, data freshness, or any constraints, which could be helpful for an agent. It meets the minimum viable standard but has clear gaps in behavioral context.
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 add parameter details, which is appropriate here, but it could have mentioned any implicit filters or options, though not required. Baseline is 4 due to zero 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 action ('List all available') and the resource ('BAFU hydrological monitoring stations (rivers and lakes) in Switzerland'), making the purpose specific and unambiguous. It distinguishes itself from sibling tools like 'list_air_quality_stations' or 'list_snow_stations' by specifying the type of stations.
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 needing hydrological station data, but it does not explicitly state when to use this tool versus alternatives like 'get_nearby_stations' or 'search_stations', nor does it mention any prerequisites or exclusions. The context is clear 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return data (daily/weekday counts, heavy vehicle percentage, measurement year) and the geographic scope (Switzerland), but it lacks details on error handling, rate limits, or data freshness. It does not contradict any 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 a single, well-structured sentence that efficiently conveys the tool's function, input, and output without any wasted words. It is front-loaded with the core action and includes all necessary details concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 annotations, no output schema), the description is reasonably complete. It covers purpose, input examples, and return data. However, it could improve by addressing behavioral aspects like error cases or data limitations, which would enhance completeness for an 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 schema description coverage is 100%, so the schema already documents the 'location' parameter. The description adds minimal value by providing example values ('Gotthard', 'Zürich', 'Genf'), but it does not explain semantics beyond what the schema provides, such as format constraints or case sensitivity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 traffic volume') and resources ('ASTRA counting station in Switzerland'), and it distinguishes from sibling tools like 'get_traffic_by_canton' and 'get_traffic_nearby' by specifying retrieval by location name rather than canton or proximity.
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 providing example locations ('Gotthard', 'Zürich', 'Genf'), but it does not explicitly state when to use this tool versus alternatives like 'get_traffic_by_canton' or 'get_traffic_nearby', nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses return fields (station code, name, altitude, canton, type) and sorting behavior, but lacks information about permissions, rate limits, pagination, or error conditions. For a read-only list tool, this is minimally adequate but misses important operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste. First sentence defines purpose and scope, second specifies return fields and sorting. Every word earns its place, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no output schema, the description provides good context about what's returned and how it's sorted. However, without annotations or output schema, it lacks information about response format, error handling, or operational constraints. The completeness is good but not 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 all three parameters. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone, with no additional value from 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 verb ('List') and resource ('SLF snow measurement stations in Switzerland'), specifying scope (IMIS automatic stations and manual study plots). It distinguishes from siblings like 'get_snow_conditions' or 'get_snow_measurements' by focusing on station metadata rather than weather data or measurements.
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 station lists, but provides no explicit guidance on when to use this tool versus alternatives like 'list_weather_stations' or 'get_nearby_stations'. It mentions sorting by elevation descending, which gives some context, but lacks explicit when/when-not statements or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that results are sorted by most recent first and mentions the optional inclusion of quarry blasts, but doesn't cover important behavioral aspects like rate limits, authentication requirements, error handling, or response format. The description doesn't contradict any annotations since none 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 perfectly concise with two sentences that efficiently convey purpose, source, sorting behavior, and optional features. Every word serves a purpose with no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only data retrieval tool with 4 well-documented parameters but no output schema, the description is adequate but could be more complete. It explains what data is retrieved and basic filtering options, but doesn't describe the response structure, data fields returned, or potential limitations beyond what's in the parameter descriptions.
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 100% description coverage, providing default values and constraints for all 4 parameters. The description adds value by explaining the overall purpose of filtering ('recent seismic events') and specifically mentioning the quarry blast inclusion option, which gives context 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 action ('Get recent seismic events'), specifies the resource ('from the Swiss Seismological Service at ETH Zürich'), and defines the scope ('in and around Switzerland'). It distinguishes from sibling tools like 'get_earthquake_details' and 'search_earthquakes_by_location' by focusing on recent events without location filtering.
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 recent seismic data, but doesn't explicitly state when to use this tool versus alternatives like 'get_earthquake_details' (for specific events) or 'search_earthquakes_by_location' (for location-based filtering). It mentions optional inclusion of quarry blasts, which provides some context for when to use this feature.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as a list operation and specifies the returned data fields, but lacks details on potential limitations (e.g., rate limits, data freshness, authentication requirements) or operational traits (e.g., pagination, error handling). This is a significant gap for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose, scope, and output. It is front-loaded with the core action and includes no 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.
Completeness3/5
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, no output schema, no annotations), the description is adequate but not fully complete. It specifies the data returned but does not address behavioral aspects like performance, errors, or data format, which are important for an agent to use it effectively. The lack of annotations increases the need for more contextual detail.
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 0 parameters with 100% schema description coverage (empty schema). The description does not need to explain parameters, and it appropriately avoids redundant information. A baseline of 4 is applied since no parameters exist, and the description does not introduce unnecessary complexity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 ('List all official Swiss NABEL air quality monitoring stations') and resources (stations operated by BAFU/EMPA), including what data is returned. It distinguishes itself from sibling tools like 'get_air_quality' by focusing on station metadata rather than air quality measurements.
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 specifying the scope (Swiss NABEL stations operated by BAFU/EMPA) and the type of data returned. However, it does not explicitly state when to use this tool versus alternatives like 'get_nearby_stations' or 'list_weather_stations', nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about the data source ('Source: Swiss federal geodata (swisstopo)'), which helps establish credibility. However, it doesn't describe important behavioral aspects like response format, pagination, error conditions, or performance characteristics that would be valuable for an AI agent.
Agents need to know what a tool does to the world before calling it. Descriptions 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 with three tightly focused sentences: purpose statement, parameter guidance, and data source attribution. Every sentence earns its place, and the information is front-loaded with the core functionality stated first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one well-documented parameter and no output schema, the description provides adequate context about what the tool does and its data source. However, without annotations or output schema, it lacks information about response structure, error handling, and other behavioral details that would help an agent 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 schema description coverage is 100%, so the schema already fully documents the single 'canton' parameter with examples. The description adds marginal value by restating the acceptable formats ('2-letter canton codes or full names') but doesn't provide additional semantic context beyond what's 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 specific action ('List all Swiss postcodes') and resource ('in a given canton'), with precise scope ('all Swiss postcodes (PLZ)'). It distinguishes from sibling tools like 'lookup_postcode' and 'search_postcode' by focusing on canton-level listing rather than individual lookups 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 for when to use this tool ('List all Swiss postcodes in a given canton') and specifies acceptable input formats ('Accepts 2-letter canton codes or full names'). However, it doesn't explicitly state when not to use it or name alternative tools for related tasks like searching postcodes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the data source (Swiss federal geodata/swisstopo), which adds useful context about reliability and origin. However, it does not mention behavioral traits like rate limits, error handling, or response format, leaving gaps for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by source information. Every word earns its place with no redundancy or unnecessary details, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete. It covers the purpose, data returned, and source, but lacks details on output format, error cases, or usage constraints. For a simple lookup tool, this is adequate but leaves room for improvement in behavioral transparency.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds value by specifying the postcode format (PLZ) and providing examples ('8001' or '3000'), which enhances understanding beyond the schema's basic description. This justifies a score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 (look up) and resource (Swiss postcode), specifying the exact data returned (locality name, canton, coordinates) and distinguishing it from siblings like 'search_postcode' or 'geocode' by focusing on a single postcode lookup rather than search or coordinate-based operations.
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 needing Swiss postal code details, but does not explicitly state when to use this tool versus alternatives like 'search_postcode' or 'geocode'. It provides basic context (Swiss-specific) 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool performs comparison (not creation/modification), returns sorted results (cheapest to most expensive), and has a specific use case. However, it doesn't mention rate limits, authentication needs, error conditions, or response format 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 perfectly concise with three sentences that each earn their place: states the core function, describes the output sorting, and provides usage context. No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only comparison tool with no output schema, the description provides good context about what the tool does and returns. However, it could be more complete by mentioning the response structure (e.g., what fields are included in the comparison) or any limitations beyond the 20-municipality maximum already stated in 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?
With 100% schema description coverage, the baseline is 3. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions (e.g., it doesn't explain what H4 default means or why year range is 2011-2026).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 ('compare', 'returns') and resources ('Swiss electricity tariffs across multiple municipalities'), and distinguishes it from siblings by focusing on tariff comparison rather than single-tariff retrieval or municipality 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 the tool ('useful for relocation decisions or cost analysis') and implicitly suggests an alternative (search_municipality_energy for finding IDs), but doesn't explicitly state when not to use it or compare it to all relevant siblings like get_electricity_tariff.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the operation is a read (implied by 'Get'), specifies the data source, and indicates it returns 'all recorded votes', which adds useful context. However, it lacks details on rate limits, error conditions, or response format, leaving behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first states purpose and resource, the second specifies return scope and data source. It's front-loaded and efficiently structured, with every sentence earning its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool with no annotations and no output schema, the description is reasonably complete: it covers purpose, usage context, and behavioral aspects like data source. However, it could improve by detailing output format or error handling, given the lack of 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 documents the 'affair_id' parameter fully. The description adds minimal value beyond the schema by referencing 'OpenParlData' and 'search_parliament_business', but doesn't provide additional syntax or format details. 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 verb ('Get') and resource ('voting results for a specific parliamentary affair'), specifying it returns 'all recorded votes for the given affair ID'. It distinguishes from siblings like 'get_voting_results' by specifying the data source (OpenParlData) and scope (affair-specific).
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 by mentioning the data source (OpenParlData) and referencing 'search_parliament_business' as the way to obtain affair IDs. However, it doesn't explicitly state when not to use this tool or name alternatives among siblings like 'get_vote_details' or 'search_votes'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about the data source (BFS STATPOP) and what it returns (population figures), but does not cover other behavioral traits such as rate limits, error handling, or authentication needs. It adequately describes the operation as a read-only data fetch without contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions 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 two sentences that efficiently convey the tool's purpose, scope, and data source without any wasted words. Every sentence adds value, 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.
Completeness4/5
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, no output schema), the description is complete enough for a read-only data retrieval tool. It covers what the tool does, the data source, and the scope, though it lacks details on output format or error cases, which is a minor gap given the straightforward nature of the 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%, so the input schema already fully documents the parameters (canton and year). The description adds minimal value beyond the schema by mentioning the scope (Switzerland, canton, or all cantons) and data source, but does not provide additional syntax or format details. 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 Swiss population data') and resources ('from the Federal Statistical Office'), and distinguishes it from siblings by focusing on population data rather than other Swiss statistics like weather, traffic, or parliamentary information. It specifies the data source (BFS STATPOP) and scope (Switzerland, canton, or all cantons).
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 Swiss population data—and implies usage by specifying the data source and scope. However, it does not explicitly state when not to use it or name alternatives (e.g., 'get_statistic' might overlap), though the sibling list shows distinct tools for other data types, reducing ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool returns ('national and canton-specific holidays') and the optional canton filtering behavior. However, it doesn't disclose important behavioral traits like rate limits, authentication requirements, error conditions, or response format details that would be helpful for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions 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 front-loaded in a single sentence that communicates all essential information: what the tool does, its parameters, and what it returns. There's zero wasted language or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a read-only data retrieval tool with no annotations and no output schema, the description provides adequate context about what the tool does and returns. It could be more complete by describing the response format or any limitations, but for a simple holiday lookup tool, it's reasonably complete.
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 schema already documents both parameters thoroughly. The description adds marginal value by mentioning the canton filtering is optional and providing example canton codes, but doesn't add significant semantic meaning beyond what's in the schema. This meets the baseline expectation 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 specific action ('Get Swiss public holidays'), the resource ('public holidays'), and scope ('for a given year, optionally filtered by canton'). It distinguishes this tool from sibling tools like 'get_school_holidays' and 'is_holiday_today' by specifying it returns both national and canton-specific holidays for a given year.
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: when Swiss public holidays are needed for a specific year, with optional canton filtering. It doesn't explicitly mention when not to use it or name alternatives, but the context is sufficiently clear given the tool's specific 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the data source (SLF), update frequency (daily), return data (snow depth and new snow for IMIS stations), sorting (by snow depth), and filtering capabilities. However, it lacks details on error handling, 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 and efficiently uses two sentences to cover data source, return values, sorting, filtering, and update frequency without any 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.
Completeness4/5
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, no output schema, no annotations), the description is mostly complete, covering purpose, data source, return values, sorting, filtering, and update frequency. However, it could improve by mentioning output format or error scenarios, but it's adequate for a read-only data 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?
The input schema has 100% description coverage, so the baseline is 3. The description adds minimal value beyond the schema by mentioning filtering by canton or minimum altitude, but it does not provide additional syntax, format details, or examples beyond what is already documented 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 specific verbs ('Get current snow conditions') and resources ('across Switzerland from SLF'), distinguishing it from siblings like 'get_snow_measurements' or 'list_snow_stations' by specifying the data source (SLF) and the type of conditions (snow depth and new snow).
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 snow conditions with filtering options (by canton or altitude), but it does not explicitly state when to use this tool versus alternatives like 'get_snow_measurements' or 'list_snow_stations', nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses behavioral traits such as data types (30-min vs. daily data) and station types (IMIS vs. study plots), but lacks details on permissions, rate limits, or response format. It doesn't contradict annotations since none 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?
It's appropriately sized with three sentences, front-loaded with the main purpose, followed by data specifics and a usage tip. Every sentence adds value without waste, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 output schema, no annotations), the description is fairly complete: it explains what data is returned, station types, and how to find codes. However, it could improve by detailing response format or error handling, especially since no output schema exists.
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 both parameters. The description adds context by explaining that 'type' determines data granularity (30-min vs. daily) and referencing 'list_snow_stations' for station codes, but doesn't provide additional syntax or format details 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 verb 'Get' and resource 'detailed snow and weather measurements for a specific SLF station', specifying it's for a 'specific' station. It distinguishes from sibling tools by mentioning 'list_snow_stations' for finding station codes, though not all siblings are directly related.
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 on when to use this tool: for retrieving measurements from SLF stations, with an explicit alternative 'list_snow_stations' to find station codes. However, it doesn't specify when not to use it or compare with other weather-related tools like 'get_weather' or 'get_snow_conditions'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively communicates this is a read-only query tool (implied by 'Get') and specifies the data source and scope ('active closures'). However, it lacks details on rate limits, authentication needs, response format, or pagination behavior beyond the 'limit' parameter in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions 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 and default behavior, ending with data source attribution. Every sentence adds value with zero waste, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, no output schema, no annotations), the description is reasonably complete. It covers purpose, filtering, default behavior, and data source. However, it could improve by addressing response format or error handling, especially since no output schema exists.
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 value beyond the schema by mentioning filtering by 'reason' and 'type', but does not provide additional syntax, format details, or examples not already in the schema descriptions. 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 action ('Get'), resource ('current Swiss hiking trail closures and detours'), and data source ('official ASTRA/Schweizer Wanderwege dataset'). It distinguishes from sibling 'get_trail_closures_nearby' by not mentioning location-based filtering, focusing instead on reason/type filtering.
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 ('Filter by closure reason or type') and default behavior ('If no parameters are given, returns all active closures'). However, it does not explicitly state when NOT to use it or mention alternatives like the sibling 'get_trail_closures_nearby' for location-based queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about coordinate conversion (WGS84 to LV95) and the data source (swisstopo identify endpoint), but does not mention rate limits, authentication needs, error handling, or the format of returned closures/detours. The description adequately covers the core behavior but lacks 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, front-loading the core purpose and following with implementation details. Every sentence adds value without redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, no output schema, no annotations), the description is reasonably complete. It explains what the tool does, how it works internally, and the scope of results. However, it lacks details on output format (e.g., structure of closures/detours) and error cases, which would be helpful for an agent invoking it.
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 (lat, lon, radius). The description adds marginal value by implying these parameters are used for coordinate-based searching and radius filtering, but does not provide additional syntax or format details beyond what the schema already specifies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 ('Find Swiss hiking trail closures and detours'), resource ('near a given GPS coordinate'), and scope ('within the specified radius'). It distinguishes from sibling tools like 'get_trail_closures' by specifying the 'nearby' aspect and coordinate-based querying.
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 (finding trail closures near GPS coordinates), but does not explicitly mention when not to use it or name alternatives. It implies usage for location-based queries but lacks explicit exclusions or comparisons to similar tools like 'get_trail_closures'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return data (vote title, date, counts, etc.) and temporal scope (since 2021), but lacks details on permissions, rate limits, error handling, or data freshness. It adequately covers the core behavior but misses some operational context.
Agents need to know what a tool does to the world before calling it. Descriptions 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 (data source, return fields, scope), with 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.
Completeness4/5
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 optional parameters, no output schema, no annotations), the description is reasonably complete. It explains what the tool does, what data it returns, and its scope, but could improve by addressing potential limitations or usage constraints to fully compensate for the lack of annotations and 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, so the schema already fully documents the parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it mentions filtering by year but doesn't elaborate on format 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 ('Get results') and resources ('Swiss popular votes from Basel-Stadt open data'), distinguishing it from sibling tools like 'get_vote_details' or 'search_votes' by specifying its scope (Basel-Stadt, since 2021, returns structured vote results).
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 Swiss popular votes from Basel-Stadt since 2021), but does not explicitly mention when not to use it or name alternatives among sibling tools (e.g., 'get_vote_details' or 'search_votes'), though the scope differentiation is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by specifying the geographic limitation (Zurich city only) and data source (OpenERZ), which are important behavioral constraints. However, it doesn't mention potential error conditions, rate limits, authentication requirements, or what happens with invalid ZIP codes, leaving some behavioral aspects unclear.
Agents need to know what a tool does to the world before calling it. Descriptions 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 front-loaded with the core purpose in the first sentence, followed by important behavioral constraints. Every sentence earns its place: the first states what it does, the second specifies the return format, the third gives geographic limitations, and the fourth credits the data source. Zero waste, excellent structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only data retrieval tool with no output schema, the description provides good contextual completeness. It covers purpose, geographic scope, return format, and data source. The main gap is the lack of output schema, which means the agent doesn't know the structure of returned data, but the description compensates somewhat by specifying 'Returns all collection events grouped by date.'
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 three parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'Zurich city ZIP code' which reinforces the schema's description, but doesn't provide additional semantic context about parameter interactions or edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 full monthly waste collection calendar'), the resource ('for a Zurich city ZIP code'), and the scope ('Returns all collection events grouped by date for the given month'). It explicitly distinguishes this tool from sibling tools like 'get_waste_collection' by specifying it returns a calendar format rather than individual collection 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 ('Currently covers Zurich city only (ZIP codes 8001–8099)'), which helps the agent understand geographic limitations. However, it doesn't explicitly state when NOT to use it or mention alternatives like 'get_waste_collection' for non-calendar waste data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return behavior ('Returns the holiday name if it is one') and implies a read-only operation. However, it lacks details on error handling, rate limits, or authentication requirements that would be helpful for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions 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 with two sentences that each earn their place. The first sentence states the purpose and scope, while the second explains the return behavior. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one optional parameter and no output schema, the description is reasonably complete. It covers purpose, parameter context, and return behavior. However, without annotations or output schema, additional details about error cases or response format would improve completeness.
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 optional 'canton' parameter. The description adds marginal value by mentioning 'optionally for a specific canton' but doesn't provide additional semantics beyond what's in the 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 with a specific verb ('Check') and resource ('Swiss public holiday'), including optional canton-specific filtering. It distinguishes from sibling tools like 'get_public_holidays' by focusing on today's status rather than a list of holidays.
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 ('Check whether today is a Swiss public holiday') and implies an alternative ('get_public_holidays' for broader holiday data). However, it doesn't explicitly state when not to use it or compare directly with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the data source ('Swiss federal geodata (swisstopo)') and that it returns 'all PLZ entries matching', which adds useful context. However, it lacks details on rate limits, error handling, or response format, leaving behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste: the first states purpose and usage, the second adds data source. It is front-loaded with essential information and appropriately sized for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 annotations, no output schema), the description is reasonably complete. It covers purpose, usage, and data source, but lacks output details (e.g., format of returned entries) which would be helpful since there's no 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 the single parameter 'city_name' with examples. The description adds no additional parameter semantics beyond what the schema provides, such as partial match behavior or case sensitivity, 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 verb 'search' and resource 'Swiss postcodes', specifies the search criteria 'by city or locality name', and distinguishes from sibling tools like 'lookup_postcode' and 'list_postcodes_in_canton' by focusing on name-based search rather than code lookup or canton-based listing.
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 it searches 'by city or locality name' and 'returns all PLZ entries matching the name', which helps differentiate from alternatives. However, it does not explicitly mention when not to use it or name specific sibling tools as alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It indicates this is a read operation ('Get') and specifies the required input (ehraid), but doesn't disclose behavioral traits like rate limits, authentication needs, error conditions, or what 'full details' includes. The description adds some context but leaves important behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions 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 with two sentences that each earn their place. The first sentence states the core purpose, and the second provides essential usage guidance. There's zero wasted language or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool with no annotations and no output schema, the description provides adequate purpose and usage guidance but lacks information about what 'full details' includes in the response, error handling, or any limitations. Given the simplicity of the tool, it's minimally complete but could benefit from more behavioral 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 the schema already fully documents the single parameter. The description adds minimal value beyond the schema by reinforcing that ehraid comes from search_companies and is an integer, but doesn't provide additional semantic context about the parameter's meaning or usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 full details of a Swiss company by its ZEFIX internal ID (ehraid).' It specifies the verb ('Get'), resource ('Swiss company'), and unique identifier ('ZEFIX internal ID'), and distinguishes it from sibling 'search_companies' by indicating that tool should be used first to find the ehraid.
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 provides usage guidance: 'Use search_companies first to find the ehraid — it is returned in company search results.' This tells the agent when to use this tool (after obtaining ehraid from search_companies) and names the alternative tool for initial searching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: publication schedule (~08:00 and ~17:00 Swiss time), seasonal availability (October–May), and data source (SLF/WSL). It doesn't mention rate limits or authentication needs, but covers essential operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tightly focused sentences with zero waste. First sentence states purpose and outputs, second provides critical timing context. Every word earns its place, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with no output schema, the description provides excellent context about what's returned (URLs, descriptions, map links) and operational constraints. The main gap is lack of output format details, but given the tool's relative simplicity and clear purpose, this is minor.
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 both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate coverage through structured data 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'), resource ('current Swiss avalanche danger bulletin from SLF'), and output ('Returns current bulletin URLs, danger level descriptions, and links to the interactive map'). It distinguishes from siblings like 'get_snow_conditions' or 'get_snow_measurements' by focusing on official danger bulletins rather than 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 about when to use it (daily bulletins at specific times, October–May season) and references a sibling tool ('list_avalanche_regions') for parameter options. However, it doesn't explicitly state when NOT to use it or name alternatives for similar data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that it returns 'all available fields' and lists them, which adds useful context about output richness. However, it doesn't mention behavioral traits like error handling (e.g., if the dam name isn't found), rate limits, or data freshness, leaving gaps for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions 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 details on returned fields and data source. 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.
Completeness4/5
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 output schema, no annotations), the description is mostly complete. It explains what the tool does, what it returns, and the data source. However, without an output schema, it could benefit from more detail on return format or error cases, but the listed fields provide good coverage.
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?
Schema description coverage is 100%, so the schema already documents the single parameter 'name' with examples. The description adds value by emphasizing it's for a 'specific Swiss dam by name', reinforcing the parameter's purpose, but doesn't provide additional semantics beyond what the schema offers. With only one parameter, a baseline of 4 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 verb ('Get full technical details') and resource ('specific Swiss dam by name'), and distinguishes it from sibling tools like 'search_dams' and 'get_dams_by_canton' by specifying it returns comprehensive details for a single dam. It explicitly mentions the data source, adding specificity.
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 full technical details of a specific dam by name), and the input schema suggests using 'search_dams' first if unsure of the exact name, offering an alternative. However, it doesn't explicitly state when not to use it or compare it to all relevant siblings like 'get_dams_by_canton'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns up to 20 dams with basic details (implying a limit and partial data), specifies the data source (SFOE via swisstopo BGDI), and indicates it's a read operation ('List'). It does not cover aspects like error handling, authentication needs, or rate limits, but provides useful operational context.
Agents need to know what a tool does to the world before calling it. Descriptions 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 operational details (limit, data source) in two efficient sentences. Every sentence adds value without redundancy, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 output schema, no annotations), the description is largely complete: it states purpose, scope, data source, and result limits. However, it lacks details on output format (e.g., what 'basic details' include) and error cases, which could enhance completeness 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, fully documenting the 'canton' parameter with examples. The description adds no additional parameter semantics beyond what the schema provides, such as format details or constraints, 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 specific action ('List all Swiss dams under federal supervision'), resource ('dams'), and scope ('in a given canton'), distinguishing it from sibling tools like 'get_dam_details' or 'search_dams' by specifying the canton-based filtering and federal supervision criteria.
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 'in a given canton' and 'under federal supervision,' which helps differentiate it from broader search tools. However, it does not explicitly state when to use this tool versus alternatives like 'search_dams' or 'get_dam_details,' missing 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It indicates this is a read operation ('Get'), but lacks details on behavioral traits such as rate limits, error handling, or response format. The description adds some context by specifying the source of event IDs, but more behavioral information would be helpful given the absence of 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 two sentences with zero waste: the first states the purpose, and the second provides usage guidelines. It is front-loaded with the core functionality and efficiently conveys necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 output schema, no annotations), the description is reasonably complete. It covers purpose and usage well, but lacks details on behavioral aspects like response format or error conditions, which could be beneficial for an agent invoking the 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?
The schema description coverage is 100%, so the schema already documents the event_id parameter with examples and sourcing instructions. The description adds no additional parameter semantics beyond what's in the schema, meeting 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: 'Get full details for a specific seismic event by its SED event ID.' It specifies the verb ('Get'), resource ('seismic event'), and scope ('full details'), and distinguishes it from siblings like get_recent_earthquakes and search_earthquakes_by_location by indicating it's for detailed information on a single event.
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: 'Use event IDs returned by get_recent_earthquakes or search_earthquakes_by_location.' This provides clear guidance on prerequisites and distinguishes it from alternatives that list or search for earthquakes, making it easy for an agent to select correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: the data source (ElCom), valid year range (2011-2026), and return format (total price and breakdown by component). It doesn't mention rate limits or authentication requirements, but covers the essential operational context.
Agents need to know what a tool does to the world before calling it. Descriptions 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 with two sentences that are front-loaded with essential information. Every word earns its place, providing maximum information density without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only data retrieval tool with no output schema, the description provides good context about what data is returned (price breakdown) and constraints (year range). It could be more complete by specifying the exact format of the breakdown or mentioning data freshness, but covers the essential operational needs.
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 baseline is 3. The description adds some context about valid years and price breakdown, but doesn't provide additional parameter semantics beyond what's already well-documented in the schema descriptions for municipality, category, and year.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 ('Get'), resource ('Swiss electricity tariff'), and specific scope ('for a municipality from ElCom'). It distinguishes from siblings by focusing on electricity tariffs rather than other data types like weather, traffic, or population.
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 it (for Swiss electricity tariffs from ElCom) and mentions a sibling tool ('search_municipality_energy') for finding municipality IDs. However, it doesn't explicitly state when NOT to use it or compare with 'compare_electricity_tariffs'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool returns monthly average rates, supports optional date filtering, and defaults to the most recent 90 months if no date range is given. This covers output format, default behavior, and optionality, though it lacks details on rate limits, error handling, 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 and concise, with two sentences that efficiently convey the tool's purpose, output, and default behavior. Every sentence adds essential information without redundancy, 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.
Completeness4/5
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, no output schema, no annotations), the description is largely complete. It covers purpose, usage context, and behavioral traits adequately. However, without an output schema, it could benefit from more details on return values (e.g., data structure or examples), slightly limiting completeness for an agent's 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?
The input schema has 100% description coverage, providing full details on parameters (currency, from, to). The description adds minimal value beyond the schema, only implying date filtering without specifying format or constraints. Since the schema does 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 tool's purpose with specific verbs ('Get historical CHF exchange rates') and resources ('from the Swiss National Bank (SNB)'), distinguishing it from sibling tools like 'get_exchange_rate' (likely current rates) and 'list_currencies' (currency listing). It specifies the scope (monthly average rates) and data source, 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 usage: it mentions using 'list_currencies' to see available currency codes, which is an explicit prerequisite. However, it does not specify when to use this tool versus alternatives like 'get_exchange_rate' (e.g., for historical vs. current rates), nor does it outline any exclusions or constraints beyond date filtering.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions fetching 'details and resource links,' which gives some behavioral insight, but lacks information on permissions, rate limits, error handling, or response format. The description adds basic context but misses key 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 two concise sentences with zero waste. The first sentence states the purpose, and the second provides usage guidance, making it front-loaded and efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, no output schema) and lack of annotations, the description is reasonably complete. It covers purpose, usage, and distinguishes from siblings, but could improve by adding more behavioral details like response format or error cases.
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 'dataset_id' fully described in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. 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 action ('Fetch details and resource links') and the resource ('a specific BFS/OFS dataset by its opendata.swiss identifier'). It distinguishes from sibling 'search_statistics' by specifying this tool is for fetching details of a known dataset ID, while search_statistics is for finding IDs.
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 provides when to use this tool ('by its opendata.swiss identifier') and when to use an alternative ('Use search_statistics first to find dataset IDs'). This gives clear guidance on the workflow and distinguishes it from the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it returns 'next scheduled pickups sorted by date', specifies geographic coverage constraints, mentions the data source, and implies it's a read-only query (consistent with 'get' prefix). It doesn't mention rate limits or error handling, but covers the essential operational context.
Agents need to know what a tool does to the world before calling it. Descriptions 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 front-loaded with the core purpose in the first sentence, followed by important constraints and data source. Every sentence earns its place with zero wasted words, making it highly efficient and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only query tool with 100% schema coverage but no output schema, the description provides strong context about geographic scope, sorting behavior, and data source. It could slightly improve by hinting at the return format (e.g., 'Returns list of dates with waste types') since there's no output schema, but it's largely complete for its complexity level.
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 adds no additional parameter semantics beyond what's in the schema descriptions, maintaining the baseline score of 3 for adequate but not enhanced 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 upcoming waste collection dates'), resource ('for a Zurich city ZIP code'), and scope ('Currently covers Zurich city only (ZIP codes 8001–8099)'). It distinguishes from sibling tools like 'get_waste_calendar' by specifying it returns sorted upcoming dates rather than a full calendar.
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 geographic limitations ('Zurich city only') and data source ('Powered by OpenERZ'), which helps determine when to use it. However, it doesn't explicitly mention when not to use it or name alternatives among sibling tools (e.g., 'get_waste_calendar').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns a list of regions with specific fields, which is basic behavioral info. However, it lacks details on permissions, rate limits, pagination, or error handling, leaving gaps for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste: the first states purpose and returns, the second provides usage guidance. It is front-loaded with essential information and efficiently structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with no annotations, no output schema, and one optional parameter, the description is mostly complete. It covers purpose, returns, and usage context. However, it lacks output format details (e.g., structure, pagination) and error scenarios, which could be helpful for an 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 optional 'canton' parameter. The description adds no additional parameter semantics beyond what the schema provides, such as examples of canton abbreviations or filtering effects. 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 verb ('List') and resource ('all Swiss avalanche warning regions'), specifies the data source ('SLF/EAWS'), and enumerates the returned fields ('region IDs, names, cantons, and typical elevations'). It distinguishes from siblings by focusing on avalanche regions, unlike other tools for weather, traffic, or data 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?
It explicitly states when to use this tool ('Use region IDs with get_avalanche_bulletin'), providing a direct alternative and integration guidance. This helps the agent understand the tool's role in a workflow, though it doesn't specify when not to use it or compare with non-sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the geographic limitation (Zurich city only) and data source (OpenERZ), but lacks details on rate limits, error handling, or response format. It adequately describes the scope but misses deeper behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions 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 and source details in two efficient sentences. Every sentence adds necessary information without waste, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, scope, and source, but could improve by hinting at the return structure (e.g., list format) or limitations, though not strictly required.
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 adds value by explaining the tool's scope and data source, which compensates for the lack of parameters, earning a baseline 4 for zero-param tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 ('List') and resource ('supported waste collection types for Zurich city'), and distinguishes it from siblings like 'get_waste_calendar' or 'get_waste_collection' by focusing on types rather than schedules or 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?
It provides clear context on when to use this tool ('Currently covers Zurich city only (ZIP codes 8001–8099)'), but does not explicitly mention when not to use it or name alternatives among siblings, such as for other cities or broader waste 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it searches both dam and reservoir names, supports partial name matching (implied by 'Partial names are supported' in schema), returns specific attributes (dam type, height, etc.), and cites the data source (SFOE via swisstopo BGDI). It does not mention rate limits, authentication needs, or pagination, but covers the core operational behavior well.
Agents need to know what a tool does to the world before calling it. Descriptions 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 useful context (search scope, return fields, data source). Every sentence adds value without redundancy, making it efficient and well-structured for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 one parameter), no annotations, and no output schema, the description is reasonably complete. It explains what the tool does, what it returns, and the data source. However, it lacks details on output format (e.g., JSON structure) or error handling, which could be helpful for an agent invoking it.
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 the 'query' parameter fully documented in the schema itself. The description adds minimal value beyond the schema, only reiterating that it searches 'by name' without providing additional syntax or format details. 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 ('search'), resource ('Swiss dams and reservoirs under federal supervision'), and scope ('by name'). It explicitly distinguishes this tool from sibling tools like 'get_dam_details' and 'get_dams_by_canton' by specifying it searches both dam and reservoir names, unlike those which appear to retrieve specific details or filter by canton.
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 search dams/reservoirs by name. It implicitly suggests alternatives by mentioning sibling tools like 'get_dam_details' (for detailed info on a specific dam) and 'get_dams_by_canton' (for filtering by location). However, it does not explicitly state when NOT to use this tool or compare it directly to all alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the tool's purpose and output format (BFS municipality numbers), but lacks details on behavioral traits like error handling, rate limits, or authentication requirements. It doesn't contradict any annotations, but could be more informative about operational constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste—each sentence adds critical information (purpose and usage context). It's front-loaded with the main action and efficiently structured to convey essential details without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 output schema, no annotations), the description is mostly complete. It covers purpose, usage, and output format, but lacks details on behavioral aspects like performance or errors. For a simple search tool, this is adequate but not fully 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 already documents the single parameter ('name') with examples. The description doesn't add any parameter-specific details beyond what the schema provides, such as search behavior or result limits. 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 for Swiss municipality IDs') and resource ('Swiss municipality'), distinguishing it from siblings like 'get_municipality' by specifying its purpose for electricity tariff lookup. It uses precise verbs and identifies the exact output (BFS municipality numbers).
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 ('needed for electricity tariff lookup') and names specific alternatives ('for use with get_electricity_tariff and compare_electricity_tariffs'), providing clear guidance on its role in the workflow and distinguishing it from other search 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively explains key behavioral traits: it generates URLs rather than fetching tracking data directly, requires opening in a browser due to lack of API, and specifies the service provider (Swiss Post). It doesn't mention rate limits, authentication needs, or error handling, but covers the core operational behavior well.
Agents need to know what a tool does to the world before calling it. Descriptions 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 with two sentences that each earn their place: the first states the core purpose, the second explains the critical behavioral constraint (no API, browser required). There's zero wasted language and it's front-loaded with the main functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no annotations and no output schema, the description provides good contextual completeness. It explains what the tool does, why it works this way (no API), and what to expect (a URL to open). It could slightly improve by mentioning the return format explicitly, but covers the essential context given the tool's simplicity.
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 single parameter. The description adds marginal value by reinforcing that the tracking number is for Swiss Post and mentioning example formats, but doesn't provide additional syntax or validation details beyond what the schema 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 specific action ('Generate a Swiss Post parcel tracking URL') and the resource involved ('for a given tracking number'). It distinguishes this tool from all sibling tools, which are primarily data retrieval tools for various Swiss public services, by focusing on URL generation rather than direct data access.
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 Swiss Post tracking is needed and no API exists, requiring a browser-based approach. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, though the distinction is implied by the unique purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes what information is returned (location, environment type, legal limits, live data link), which is useful. However, it lacks details on behavioral traits such as rate limits, error handling, or data freshness, which are important for a data retrieval tool. The description doesn't contradict any annotations (since none 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, followed by usage instructions and examples. Every sentence earns its place by providing essential information 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.
Completeness4/5
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, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and parameter semantics effectively. However, it could improve by mentioning output format or any limitations (e.g., data availability), though the lack of output schema means some gaps are expected.
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?
Schema description coverage is 100%, so the schema already documents the 'station' parameter. The description adds value by explaining the semantics: station codes come from 'list_air_quality_stations' and provides examples (BER=Bern, ZUE=Zürich, LUG=Lugano), which clarifies usage beyond the schema's basic description. With only one parameter, this is sufficient for a high score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships 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 information about') and resource ('Swiss NABEL air quality monitoring station'), including detailed content (location, environment type, legal limits, live data link). It distinguishes from the sibling tool 'list_air_quality_stations' by specifying that tool provides station codes, while this one retrieves detailed information for a specific station.
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?
Explicit guidance is provided: use station codes from 'list_air_quality_stations' (e.g., BER, ZUE, LUG), and examples are given. This clearly indicates when to use this tool (for detailed station info) versus the sibling tool (for listing station codes), with no 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool retrieves published monthly data from BFS, provides historical context (baseline December 1982 = 100), and clarifies scope (cost of living including residential rents). However, it doesn't mention potential limitations like data availability or update frequency.
Agents need to know what a tool does to the world before calling it. Descriptions 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 with zero waste. The first sentence states the core purpose and scope, while the second provides crucial differentiation from sibling tools. Every element serves a clear purpose without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only data retrieval tool with no annotations and no output schema, the description provides strong contextual completeness. It explains what the index measures, its baseline, publication frequency, and source. The main gap is lack of information about return format or data structure, which would be helpful given no 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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, but it does provide context about the data (monthly publication, baseline) that helps interpret parameter usage. 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 with specific verbs ('Get') and resources ('Swiss Consumer Price Index (CPI/LIK)'), explicitly distinguishing it from sibling tools by mentioning 'For property purchase prices, use get_property_price_index instead.' This provides clear differentiation and avoids redundancy with the tool name.
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 by specifying when to use this tool (for residential rent tracking) and when to use an alternative ('For property purchase prices, use get_property_price_index instead'). It also includes context about the data source and baseline, which helps the agent understand appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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