lazymac-mcp

Server Quality Checklist

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

Latest release: v1.0.2
Disambiguation3/5
Most tools have distinct purposes (e.g., ai_budget_planner vs. ai_spend_tracker), but there is some overlap in the AI cost/usage category (ai_cost_calculator, ai_spend_tracker, llm_pricing) and Korean data tools (k_address_geocoder vs. k_address_tools). Descriptions help differentiate, but an agent might struggle with subtle distinctions between similar tools.
Naming Consistency4/5
Tool names follow a consistent snake_case pattern throughout, with clear verb_noun or noun_verb structures (e.g., ai_budget_planner, cron_parser). Minor deviations exist (e.g., 'diff_patch_tools' uses plural 'tools' while others use singular nouns), but overall naming is predictable and readable.
Tool Count2/5
With 41 tools, the count is excessive for a single server, making it feel bloated and unfocused. While the tools cover diverse utility functions, such a high number can overwhelm agents and suggests poor scoping, as many tools (e.g., color_palette, placeholder_image) could be grouped into broader categories.
Completeness4/5
The tool set broadly covers utility domains like AI cost management, data processing, Korean services, and web tools, with few obvious gaps. However, some areas lack depth (e.g., AI tools focus on cost but not performance monitoring), and the wide scope makes it hard to assess true completeness for any single domain.
Average 2.8/5 across 41 of 41 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. However, it does not explain what the tool does operationally (e.g., calculates, estimates, analyzes), what inputs it expects beyond the generic 'params', or what outputs it produces. There is no mention of rate limits, authentication needs, or other behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (a single phrase listing methods) but lacks structure and front-loading of key information. It does not start with a clear purpose statement. While it's brief, it under-specifies rather than being efficiently informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by valuation methods and the lack of annotations and output schema, the description is highly incomplete. It does not explain what the tool does, how to use it, what results to expect, or how it differs from siblings. This is inadequate for a tool that likely performs calculations or analyses.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage but only describes 'params' as a free-form object passed as query string or JSON body. The tool description does not add any meaning about what parameters are needed (e.g., financial data, method preferences) or their semantics. With 1 parameter and high schema coverage, the baseline is 3, but the description fails to compensate by explaining parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists 7 valuation methods (DCF, Berkus, Scorecard, VC, comparables) but does not state what the tool actually does with them. It's unclear whether this tool calculates valuations, explains methods, compares them, or something else. The name 'startup_valuation' suggests it calculates valuations, but the description is vague about the specific action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 does not mention any context, prerequisites, or comparisons with sibling tools like 'ai_budget_planner' or 'ai_cost_calculator'. This leaves the agent without direction on appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. However, it only lists features without explaining how the tool behaves—e.g., whether it's read-only, requires authentication, has rate limits, or what output to expect. This leaves critical behavioral aspects undocumented.
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 concise with three brief phrases, avoiding unnecessary words. However, it lacks structure—front-loading key actions or purposes would improve clarity. While efficient, it could be better organized to prioritize the tool's main function.
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 (inferred from multiple features like metadata, pairings, and CSS generation), no annotations, and no output schema, the description is incomplete. It fails to explain how these features interact, what the output includes, or any prerequisites, leaving significant gaps for an agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting a single 'params' object as free-form for GET/POST. The description adds no parameter-specific details beyond what the schema provides, such as example parameters or usage. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '102 Google Fonts metadata, 35 curated pairings, CSS generation' lists three capabilities but lacks a clear action verb or specific purpose. It states what resources are available but not what the tool actually does with them (e.g., retrieve, generate, or analyze). This makes the purpose vague rather than clearly defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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, what problems it solves, or how it differs from sibling tools like 'color_palette' or 'text_analysis'. Without any context or alternatives mentioned, it offers no usage instructions, leaving the agent to guess its application.
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 functional capabilities but doesn't disclose behavioral traits like whether operations are read-only or mutating, what permissions might be needed, rate limits, or what format results are returned in. For a tool with a free-form params object, this is particularly problematic.
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 extremely concise - just four comma-separated functional areas. While efficient, it may be too terse given the complexity implied by the free-form params object. Every word earns its place, but more structure could improve 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 complexity implied by a free-form params object, no annotations, no output schema, and multiple functional areas, the description is inadequate. It doesn't explain how to use the tool, what inputs are expected, what outputs are returned, or any behavioral constraints. This leaves too many unknowns for effective tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but the schema only describes a generic 'params' object without specifying what parameters are actually supported. The description lists functional areas but doesn't map them to specific parameters or explain what inputs are expected for each operation. With 1 parameter that's completely undocumented, the description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists functional areas (conversion, harmonies, contrast, gradients) but doesn't specify a clear verb-action relationship or what resource is being operated on. It's vague about whether this generates, analyzes, or transforms color data. While it distinguishes from unrelated siblings, it lacks specificity about the core operation.
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 about when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions. With many sibling tools available, there's no indication of when color palette operations are appropriate versus other data transformation or analysis 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 the full burden of behavioral disclosure. It hints at functionality (entropy, crack-time, common-password check) but lacks details on inputs, outputs, error handling, or performance traits. For a tool with a complex parameter object, this is insufficient to guide the agent effectively.
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 very concise with a single phrase, which is efficient. However, it's under-specified rather than optimally structured—it could benefit from front-loading the core purpose more clearly, but it avoids unnecessary verbosity.
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 complexity (1 parameter with nested objects, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or provide enough context for the agent to use it correctly, making it inadequate for the tool's likely functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, but the description adds no meaningful parameter semantics beyond what the schema provides. The schema describes 'params' as a free-form object, but the description doesn't clarify what specific parameters (e.g., password string) are expected, leaving a gap in understanding despite the high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description mentions 'Entropy, crack-time, common-password check' which indicates it analyzes password strength, but it's vague about the specific action (e.g., 'calculate' or 'evaluate'). It distinguishes from siblings by noting it's a 'zxcvbn alt', but the purpose isn't clearly stated as a verb+resource combination like 'Evaluate password strength using entropy and crack-time metrics'.
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 any context, prerequisites, or exclusions, leaving the agent without direction on appropriate usage scenarios.
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 features but doesn't disclose behavioral traits like required inputs, output format, error handling, rate limits, or authentication needs. For a tool with a complex parameter object and no output schema, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a list of features in a single phrase, but it's under-specified rather than efficiently informative. It's front-loaded but lacks structure to clarify the tool's function. Every word earns its place, but more content is needed for completeness.
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 complexity implied by features like multi-exchange and DeFi, no annotations, a nested parameter object, and no output schema, the description is incomplete. It doesn't explain how to use the tool, what inputs are expected, or what results to anticipate, leaving 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?
The input schema has 1 parameter with 100% description coverage, stating it's a 'free-form params object' for GET/POST. The description lists features (e.g., FIFO, DeFi) but doesn't explain how these map to parameters or add meaning beyond the schema. With high schema coverage, the baseline is 3, and the description doesn't compensate further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists features (FIFO/LIFO/Average cost basis, multi-exchange, DeFi, staking) but lacks a clear verb stating what the tool does. It implies calculation but doesn't explicitly state 'calculate crypto taxes' or similar. It distinguishes from siblings by focusing on crypto tax calculation, but the purpose is vague rather than 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?
No guidance on when to use this tool versus alternatives is provided. The description doesn't mention context, prerequisites, or exclusions. Sibling tools include other calculators (ai_cost_calculator, startup_valuation) and crypto-related tools (crypto_signal), but no differentiation is offered.
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 operations (diffing, patch generation/application) but doesn't disclose critical behavioral traits such as input/output formats, error handling, performance characteristics, or side effects. For a tool with a single complex parameter object, this is insufficient 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 very concise with just one phrase ('LCS-based text diffing, unified patch generate / apply'), which efficiently communicates the core functionality. However, it could be more structured by separating the diffing and patching aspects for clarity, and it lacks any introductory 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?
Given no annotations, no output schema, and a single but complex nested parameter object, the description is incomplete. It doesn't explain what the tool returns, how to structure the 'params' object for specific operations, or provide examples. For a tool with potentially varied use cases (diffing vs. patching), more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'params' documented as a free-form object passed differently based on HTTP method. The description adds no additional parameter semantics beyond what the schema provides, but since schema coverage is high, 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'LCS-based text diffing, unified patch generate / apply' which identifies the core operations (diffing, patch generation/application) and mentions the algorithm (LCS). However, it's somewhat vague about the exact resource being manipulated (text) and doesn't clearly distinguish from potential sibling tools like 'text_analysis' or 'data_transform' that 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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention any prerequisites, typical use cases, or exclusions. Given sibling tools like 'text_analysis' and 'data_transform' exist, this lack of differentiation leaves the agent guessing about appropriate contexts.
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 only lists capabilities without behavioral context. It doesn't disclose whether operations are read-only or mutative, what permissions might be needed, rate limits, or what happens during validation failures—critical gaps for a tool with multiple functions.
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 extremely concise—a single phrase listing functions—with no wasted words. However, it lacks front-loaded clarity about the primary purpose, slightly reducing effectiveness despite efficient wording.
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 multiple complex functions (validation, code generation, mocking, etc.), no annotations, no output schema, and a vague description, this is inadequate. The description doesn't explain return values, error conditions, or how the listed capabilities interrelate, leaving significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema documents the single 'params' parameter adequately. The description adds no parameter-specific information beyond what the schema provides, meeting the baseline for high coverage but not compensating with additional semantic context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists multiple functions (auto-infer schemas, validate, generate TypeScript, mock data, diffs) but doesn't specify a primary verb+resource combination or distinguish from sibling tools. It's vague about what the tool actually does as a cohesive operation rather than just listing capabilities.
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 on when to use this tool versus alternatives is provided. The description doesn't mention any context, prerequisites, or exclusions for usage, leaving the agent with no directional help 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, the description carries full burden but provides minimal behavioral insight. It mentions operations (normalize, parse, etc.) but doesn't disclose critical traits like rate limits, authentication needs, error handling, or output format. For a tool with potentially complex address processing, 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 concise list of functions without unnecessary words. However, it's not front-loaded with a clear primary purpose, and the lack of structure (e.g., bullet points or prioritization) makes it less effective despite brevity.
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 one parameter (a nested object), no annotations, no output schema, and multiple implied functions, the description is incomplete. It doesn't clarify input formats, output expectations, or error conditions, leaving significant gaps for an AI agent to infer usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but the schema only documents a generic 'params' object without specifics. The description adds no parameter details beyond the schema, failing to explain what parameters are needed for each function (e.g., address string for geocode). Baseline 3 applies due to high coverage, but value added is minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists functions (normalize, parse, validate, geocode, postal code) but lacks a clear verb+resource structure and doesn't distinguish from sibling 'k_address_geocoder'. It's vague about what specific Korean address operations it performs and how they differ from the dedicated geocoder 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 on when to use this tool versus alternatives like 'k_address_geocoder' or 'ip_geolocation'. The description implies multiple functions but doesn't specify contexts for each (e.g., when to normalize vs. geocode). No prerequisites or exclusions 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It hints at output types but doesn't specify whether this is a read-only operation, what formats are supported, any rate limits, or how the tool behaves with different parameters. This leaves significant gaps in understanding the tool's behavior 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, listing three output types in a single phrase without unnecessary words. However, it could be more structured by front-loading a clear action verb, but it efficiently communicates the core idea without waste.
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 complexity of a tool with a free-form parameter object and no output schema, the description is insufficient. It doesn't explain what the tool returns, how to interpret the parameters, or any behavioral traits, making it incomplete for effective agent use despite the high schema coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools 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, indicating the single parameter 'params' is a free-form object passed as query string or JSON body. The description doesn't add any meaning beyond this, such as examples of common parameters or their effects, but since schema coverage is high, the baseline score of 3 is appropriate as the schema handles the documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists three types of outputs (SVG/PNG placeholders, avatar initials, geometric patterns) which gives a general sense of what the tool generates, but it lacks a clear verb specifying the action (e.g., 'generate' or 'create') and doesn't distinguish it from sibling tools like 'color_palette' or 'qr_code' that also produce visual outputs. The purpose is somewhat vague without explicit action verbs.
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 any specific contexts, prerequisites, or exclusions, and it fails to differentiate from sibling tools that might serve similar visual generation purposes, leaving the agent without clear usage instructions.
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 optimizing for 'cost, quality, and consistency' but does not explain how this is achieved (e.g., via API calls, local processing, or external services), what the output looks like, or any limitations like rate limits or authentication needs. This is inadequate for a tool with potential complexity.
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 and easy to parse, though it could benefit from slightly more detail to improve clarity without sacrificing brevity.
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 tool returns, how optimizations are applied, or any behavioral traits. For a tool with potential complexity (implied by optimizing multiple aspects), this minimal description fails to provide sufficient context for effective use by 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 input schema has 100% description coverage, documenting a single 'params' object as free-form parameters passed in queries or JSON. The description does not add any semantic details beyond this, such as example parameters or expected fields. With high schema coverage, the baseline score of 3 is appropriate, as the description provides no extra parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Optimize prompts for cost, quality, and consistency' states a clear purpose with a specific verb ('optimize') and target ('prompts'), but it lacks detail on what optimization entails or how it differs from sibling tools like 'prompt_shield' or 'text_analysis'. It's vague about the mechanism (e.g., whether it suggests edits, analyzes metrics, or generates alternatives).
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 does not mention context, prerequisites, or exclusions, and it fails to differentiate from sibling tools such as 'prompt_shield' (which might protect prompts) or 'text_analysis' (which could analyze prompts). This leaves the agent without direction on appropriate usage scenarios.
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 lists functional capabilities but doesn't describe how the tool behaves—e.g., whether it processes text synchronously or asynchronously, any rate limits, authentication needs, output format, or error handling. This leaves significant gaps in understanding the tool's operational traits.
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 very concise—just four terms separated by commas—and front-loaded with the key features. It wastes no words, but it's arguably too terse, lacking a complete sentence or structure that could better convey purpose. However, every term earns its place by indicating capabilities, so it scores well for efficiency.
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 complexity (multiple analysis functions), no annotations, no output schema, and a nested input schema with free-form parameters, the description is incomplete. It doesn't explain how to use the tool effectively, what results to expect, or handle the ambiguity of the 'params' object. For a tool with such open-ended inputs and no structured outputs, more guidance is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting a single 'params' object as free-form parameters passed in queries or JSON. The description adds no parameter-specific details beyond the schema, such as examples of common params (e.g., 'text' field) or how to structure them for different analyses. With high schema coverage, the baseline is 3, but the description doesn't compensate with additional semantic context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists four analysis functions (sentiment, readability, keyword extraction, language detection), which gives a general idea of what the tool does, but it's vague about the specific action (e.g., 'analyzes text for' or 'performs text analysis on') and doesn't clearly distinguish it from sibling tools like 'seo_analyzer' or 'prompt_optimizer' that might overlap in text processing. It states what features are available but not the core verb+resource combination explicitly.
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 context, prerequisites, or exclusions, and with many sibling tools (e.g., 'seo_analyzer', 'prompt_optimizer') that could handle similar text tasks, there's no indication of how this tool differs or when it's the preferred choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 fails to describe how the tool operates (e.g., read-only vs. mutative, authentication needs, rate limits, or data freshness). The vague mention of 'Korean financial data' offers no insight into behavioral traits beyond the resource scope.
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 very concise—a single phrase listing data types—with no wasted words. It's front-loaded with the core information, though it could be more structured by including a verb. The brevity is efficient but risks under-specification.
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 complexity implied by financial data and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or any behavioral aspects like error handling. For a tool with no structured support, this leaves significant gaps for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting a single 'params' object as free-form for GET/POST usage. The description adds no parameter-specific details beyond what the schema provides, such as example keys or data formats. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
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 provides Korean financial data covering KOSPI/KOSDAQ, stocks, FX, and Kimchi Premium, which is specific about the resource domain. However, it lacks a clear verb (e.g., 'fetch', 'retrieve', 'analyze') and doesn't differentiate from sibling tools like 'k_exchange_rate' or 'k_public_data', which may overlap in financial data 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, leaving the agent to guess based on the data types listed. No explicit comparison with sibling tools like 'k_exchange_rate' is included.
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 lists functions (explain, validate, generate, next runs) but doesn't describe how these are invoked, what inputs trigger which functions, error handling, rate limits, or output format. The vague 'params' object in the schema adds to the opacity, leaving the agent unsure 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 extremely concise with four key functions listed in a dash-separated format. Every word earns its place by highlighting core capabilities without redundancy. It's front-loaded with the main purpose and efficiently structured.
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 (multiple functions), lack of annotations, vague input schema, and no output schema, the description is incomplete. It doesn't explain how to invoke specific functions, what the outputs look like, or error conditions. For a tool with such open-ended parameters, more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage but describes the single parameter as a 'Free-form params object — passed as query string for GET, JSON body for POST'. This is vague and doesn't explain what specific parameters (e.g., cron expression, action type) are needed. The description adds no parameter details beyond the schema, failing to compensate for the schema's lack of specificity.
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 specific verbs (explain, validate, generate, next runs) and resource (cron expression parser). It distinguishes itself from sibling tools by focusing on cron expressions rather than AI, data, or other utilities. However, it doesn't explicitly differentiate from potential similar cron tools that might exist elsewhere.
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 lists functions but doesn't specify contexts, prerequisites, or exclusions. Given the sibling tools are unrelated (AI, data, crypto, etc.), there's no explicit comparison to help an agent choose between similar parsing 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 the full burden of behavioral disclosure. It mentions geocoding and reverse geocoding but fails to describe how the tool behaves—such as input/output formats, error handling, rate limits, or authentication needs. This is inadequate for a tool with a complex parameter structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single phrase that efficiently conveys the core functionality. It is front-loaded with no wasted words, making it easy to parse, though this brevity contributes to gaps in other dimensions.
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 (geocoding/reverse geocoding with a nested parameter object) and lack of annotations or output schema, the description is incomplete. It doesn't cover behavioral traits, parameter details, or return values, leaving significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage but only documents a generic 'params' object without specifics. The description adds no parameter semantics, failing to explain what parameters are needed (e.g., address strings, coordinates) or their formats. This leaves the agent with insufficient guidance for proper invocation.
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 a 'Korean address geocoder + reverse geocoder (road / lot)', specifying both forward and reverse geocoding operations with Korean addresses. It distinguishes between road and lot address types, though it doesn't differentiate from sibling tools like 'k_address_tools' or 'ip_geolocation'.
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 'k_address_tools' or 'ip_geolocation'. It lacks context about typical use cases, 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. While it mentions four operations (test, match, explain, generate), it doesn't describe what each operation does, their outputs, error conditions, rate limits, authentication needs, or any side effects. The 'plain English' aspect is mentioned but not elaborated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently lists all four core capabilities plus the plain English feature. Every word earns its place with no redundancy or unnecessary elaboration.
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, no output schema, and a generic input schema that provides minimal guidance, the description is insufficient. It doesn't explain what the tool returns, how operations differ, error handling, or provide enough context for an agent to properly invoke it despite the tool's apparent complexity (multiple operations with different parameter needs).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage but only documents a generic 'params' object with free-form content. The description adds no parameter-specific information beyond what the schema already states - it doesn't explain what parameters each operation expects, their formats, or examples of valid inputs for testing, matching, explaining, or generating regex patterns.
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 specific verbs (test, match, explain, generate) and resource (regex patterns), plus the additional capability of plain English interaction. It doesn't distinguish from sibling tools, but none of the listed siblings appear to be regex-related, so differentiation isn't needed here.
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 or in what context it should be applied. It simply lists capabilities without indicating appropriate 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions DST awareness and meeting planning but lacks details on rate limits, authentication needs, error handling, or what the tool returns. The vague 'params' object in the schema further obscures expected 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 extremely concise and front-loaded, packing key information into a brief phrase. Every word earns its place by highlighting core features without unnecessary elaboration.
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 complexity implied by features like DST awareness and meeting planning, plus a vague schema and no output schema, the description is insufficient. It doesn't explain how to use the tool, what inputs are needed, or what results to expect, leaving 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.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage but only vaguely describes a 'Free-form params object' without specifying required fields, formats, or examples. The description adds no parameter details beyond what the schema states, failing to compensate for the schema's lack of specificity.
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 function as a timezone converter with DST awareness and meeting planning capabilities. It specifies the resource (400+ city timezone converter) and key features, though it doesn't explicitly differentiate from sibling tools, which are unrelated to timezone functions.
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 or in what context it should be applied. The description lists features but offers no usage scenarios, 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 'allocate', 'threshold alerts', and 'weekly digest reports', which imply mutation and notification capabilities, but doesn't specify permissions needed, whether changes are reversible, rate limits, or what the output looks like. For a tool with no annotations, this is a significant gap in behavioral 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 concise and front-loaded, listing key functionalities in a single phrase without unnecessary words. Each part ('allocate AI budget per team', 'threshold alerts', 'weekly digest reports') contributes to understanding the tool's scope efficiently.
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 (involving budget allocation, alerts, and reports), no annotations, no output schema, and a vague parameter object, the description is incomplete. It lacks details on how allocation works, what alerts entail, report formats, or error handling, making it inadequate for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 100% description coverage, stating it's a 'Free-form params object' for GET/POST. The description adds no parameter-specific details beyond the schema. With high schema coverage, the baseline is 3, as the description doesn't compensate with additional semantic meaning for parameters.
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 specific verbs ('allocate', 'threshold alerts', 'weekly digest reports') and identifies the resource ('AI budget per team'). It distinguishes from some siblings like 'ai_cost_calculator' or 'ai_spend_tracker' by focusing on allocation and reporting rather than calculation or tracking alone. However, it doesn't explicitly differentiate from all possible alternatives in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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, context for use, or exclusions. With many sibling tools like 'ai_spend_tracker' and 'ai_cost_calculator', the lack of comparative guidance leaves the agent uncertain about 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 the full burden of behavioral disclosure. It mentions estimating and comparing costs but fails to detail critical aspects like rate limits, authentication needs, response format, or whether it performs calculations locally or via API. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('Estimate token cost') and elaborates with specifics ('across GPT-4 / Claude / Gemini / Haiku — compare 30+ models'). Every word contributes value without redundancy, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of cost estimation across multiple models, no annotations, no output schema, and a single but nested parameter, the description is incomplete. It lacks details on output format, error handling, or example usage, making it insufficient for an agent to fully understand how to invoke and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools 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, documenting a single 'params' object as free-form parameters. The description adds no additional parameter semantics beyond implying model comparison, so it meets the baseline of 3 where the schema handles most of the documentation burden.
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: 'Estimate token cost across GPT-4 / Claude / Gemini / Haiku — compare 30+ models'. It specifies the action (estimate token cost) and scope (multiple AI models), though it doesn't explicitly differentiate from sibling tools like 'llm_pricing' or 'ai_spend_tracker', 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 lacks context such as prerequisites, comparison with sibling tools (e.g., 'ai_spend_tracker' for tracking vs. estimating, 'llm_pricing' for pricing details), or specific scenarios for estimation, leaving the agent with minimal direction.
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 'real-time status/latency/incident feed,' it doesn't describe what format the data comes in, whether it requires authentication, rate limits, how frequently it updates, or what happens when providers are down. For a tool with no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single phrase that efficiently communicates the core functionality. It's front-loaded with the essential information. However, it could benefit from slightly more structure (e.g., separating status, latency, and incident aspects) 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's apparent complexity (monitoring multiple AI providers in real-time), no annotations, no output schema, and a single but complex parameter (free-form object), the description is insufficient. It doesn't explain what data is returned, how to interpret it, or provide any context about the monitoring service being used. 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% (the single parameter 'params' is documented as a free-form object passed as query string for GET or JSON body for POST). The description adds no parameter-specific information beyond what's in the schema. With high schema coverage, the baseline is 3 even without additional param details 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 tool's purpose: providing real-time status, latency, and incident feed for three major AI providers (OpenAI, Anthropic, Google). It uses specific verbs ('status', 'latency', 'incident feed') and identifies the target resources. However, it doesn't differentiate from sibling tools like 'ai_rate_limit_tracker' or 'llm_router' that 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. With multiple AI-related sibling tools (e.g., ai_rate_limit_tracker, llm_router, ai_cost_calculator), there's no indication of when this status/latency/incident feed is appropriate versus those other tools. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 'track and forecast' but doesn't specify whether this is a read-only operation, requires authentication, involves rate limits itself, or what the output format looks like. For a tool with potential complexity (e.g., forecasting), 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.
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. Every part earns its place by clearly stating the tool's function, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by 'track and forecast' rate limits across multiple API keys, the lack of annotations, no output schema, and incomplete behavioral details, the description is insufficient. It doesn't explain what data is returned, how forecasting works, or any operational constraints, leaving critical 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?
The input schema has 100% description coverage, documenting the single 'params' object as free-form for GET/POST usage. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.
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 specific verbs ('track and forecast') and resource ('AI provider rate limits across all API keys'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'ai_provider_status' or 'ai_spend_tracker', which might have overlapping monitoring functions.
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 'ai_provider_status' or 'ai_spend_tracker'. There's no mention of prerequisites, specific contexts, or exclusions, leaving the agent with no usage direction beyond the basic purpose statement.
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 'budgets and alerts', hinting at monitoring and notification features, but doesn't specify how tracking works (e.g., real-time vs. batch, data sources), what permissions or authentication are needed, rate limits, or response formats. This leaves significant gaps for a tool that likely interacts with 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 functionality: 'Track AI spending across OpenAI / Anthropic / Google with budgets and alerts'. It wastes no words and clearly communicates the tool's intent without unnecessary elaboration.
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 complexity implied by tracking spending across multiple providers with budgets and alerts, the description is incomplete. No annotations exist to clarify safety or behavior, and there's no output schema to explain return values. The description lacks details on how the tool operates, what data it returns, or error handling, making it inadequate for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 100% description coverage, documenting it as a 'Free-form params object — passed as query string for GET, JSON body for POST'. The description adds no specific parameter details beyond the general purpose, so it doesn't compensate but doesn't need to given the schema coverage. Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: 'Track AI spending across OpenAI / Anthropic / Google with budgets and alerts'. It specifies the verb ('Track'), resource ('AI spending'), and scope ('across OpenAI / Anthropic / Google'), though it doesn't explicitly differentiate from sibling tools like 'ai_budget_planner' or 'ai_cost_calculator', which may have overlapping functions.
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 siblings like 'ai_budget_planner' and 'ai_cost_calculator', it's unclear if this tool is for monitoring, planning, or calculating costs, or how it relates to 'ai_rate_limit_tracker' or 'ai_token_counter'. No exclusions or specific contexts 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?
No annotations are provided, so the description carries the full burden. It mentions 'pre-flight' and 'before requests,' hinting at a read-only, non-destructive operation, but doesn't disclose behavioral traits like whether it makes external API calls, requires authentication, has rate limits, or what the output format is. For a tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Pre-flight token counting for GPT-4 / Claude / Gemini before requests.' It's front-loaded with the core purpose, uses no wasted words, and is appropriately sized for the tool's complexity. Every part earns its place.
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 no annotations, no output schema, and a single but complex parameter (nested object), the description is incomplete. It doesn't explain what the tool returns (e.g., token counts, cost estimates), how to structure the 'params' object, or behavioral details. For a tool that likely interacts with external AI models, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 100% description coverage, documenting it as a 'Free-form params object' for query/body use. The description adds no parameter-specific semantics beyond this, as it doesn't explain what 'params' should contain (e.g., model type, text input). With high schema coverage, the baseline is 3, and the description doesn't compensate further.
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: 'Pre-flight token counting for GPT-4 / Claude / Gemini before requests.' It specifies the verb ('token counting'), resource (AI model tokens), and context ('before requests'). However, it doesn't explicitly differentiate from sibling tools like 'ai_cost_calculator' or 'llm_pricing', 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 minimal guidance: it implies usage 'before requests' to AI models, suggesting a pre-check context. However, it offers no explicit when-to-use vs. when-not-to-use rules, no mention of alternatives among sibling tools (e.g., 'ai_cost_calculator'), and no prerequisites or constraints. This leaves the agent with vague direction.
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. While it mentions 'real-time signals,' it doesn't describe important behavioral aspects: whether this requires authentication, rate limits, what format the data returns, whether it's a read-only operation, or how frequently data updates. For a 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place: 'Real-time signals' establishes the temporal nature, the four indicator names specify the data types, and 'for 100+ pairs' defines the scope. There's zero wasted verbiage.
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 that this is a data retrieval tool with no annotations, no output schema, and a complex free-form parameter object, the description is insufficiently complete. It doesn't explain what format the signals come in, how to interpret them, whether there are authentication requirements, or provide examples of valid parameter configurations. For a tool that presumably returns structured financial data, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents the single 'params' parameter as a free-form object. The description adds no parameter-specific information beyond what's in the schema - it doesn't explain what specific parameters might be expected (like pair symbols, timeframes, indicator configurations) or provide examples. With high schema coverage, the baseline 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 what the tool does: provides real-time technical indicators (SMA, RSI, MACD, Bollinger) for cryptocurrency pairs. It specifies the resource (100+ crypto pairs) and the type of data (real-time signals). However, it doesn't distinguish this from sibling tools like 'k_market_intel' or 'k_exchange_rate' which might also provide crypto-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 doesn't mention when this tool is appropriate, what prerequisites might be needed, or how it differs from sibling tools like 'k_market_intel' that might offer similar market data. The agent receives no usage context beyond the basic purpose.
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 lists operations, it doesn't describe how these operations work, what formats are supported, whether transformations are destructive, what error conditions exist, or what the output looks like. For a data transformation tool with complex capabilities, this leaves significant behavioral questions unanswered.
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 extremely concise - a single comma-separated list of operations. While efficient, it may be too terse given the tool's apparent complexity. Every term in the list serves a purpose, but the lack of structure or prioritization makes it read more like a feature list than a helpful description.
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 data transformation tool with no annotations, no output schema, and a complex free-form parameter structure, the description is insufficient. It lists operations but doesn't explain how they work together, what data formats are supported, what the output looks like, or any limitations. Given the tool's apparent complexity and lack of structured documentation elsewhere, this description leaves too many questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools 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% but only documents a single 'params' object with free-form structure. The description adds no parameter-specific information beyond what's in the schema - it doesn't explain what parameters control which operations, expected formats, or required fields. Given the schema's minimal documentation, the description fails to compensate with meaningful parameter semantics.
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 lists specific operations (JSON/CSV/XML conversion, flatten, filter, sort, aggregate) that the tool performs, providing a comprehensive overview of its capabilities. However, it doesn't specify the target resource or data source being transformed, and doesn't differentiate from sibling tools like 'json_schema_validator' or 'text_analysis' 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. There's no mention of prerequisites, appropriate contexts, or comparisons with sibling tools like 'json_schema_validator' or 'text_analysis' that might handle similar data manipulation tasks. The user must infer usage from the listed operations 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, the description carries full burden but only lists validation layers without disclosing behavioral traits like rate limits, authentication needs, output format, or error handling. It mentions technical aspects but lacks 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 extremely concise and front-loaded, using a single sentence with no wasted words. Every element (8-layer, validation types) earns its place efficiently.
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 no annotations, no output schema, and a single but complex parameter (nested object), the description is incomplete. It doesn't explain how to structure the params object, what validation results look like, or handle edge cases, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the schema describing a free-form params object passed differently based on HTTP method. The description adds no parameter-specific semantics beyond the schema, so baseline 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 performs email validation with specific technical layers (syntax, MX, disposable, typo correction), providing a specific verb and resource. However, it doesn't differentiate from sibling tools, as none appear to be email-related alternatives.
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, prerequisites, or exclusions. The description lists validation layers but doesn't indicate typical use cases or constraints.
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 what is generated but fails to describe key traits like output format details, rate limits, authentication needs, or whether data is persisted. 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 extremely concise and front-loaded, listing key outputs in a single, efficient phrase without unnecessary words. Every element ('fake names, emails, addresses, JSON for testing') earns its place by specifying the tool's scope clearly.
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 tool that generates data. It does not explain the return values, such as the structure of generated JSON or how outputs are formatted, leaving the agent with insufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting a single 'params' object as free-form parameters. The description does not add any meaning beyond this, such as examples of common parameters or their effects. 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 function with specific verbs ('Generate') and resources ('fake names, emails, addresses, JSON'), making its purpose immediately understandable. However, it does not differentiate from sibling tools, as none appear to be related to fake data generation, so a 5 is not warranted.
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 specifying scenarios for testing or mentioning other tools for similar purposes. It lacks explicit context or exclusions, leaving usage unclear beyond the basic function.
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 what the tool does but does not explain how it behaves: it doesn't specify validation rules (e.g., format checks, API calls), error handling, rate limits, authentication needs, or output format. For a validation tool with zero annotation coverage, this leaves critical behavioral traits undocumented.
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 concise and front-loaded, consisting of a single, clear sentence: 'Validate Korean BRN (사업자등록번호), corp registration, phones'. It efficiently communicates the core purpose without unnecessary words, though it could benefit from additional context to improve completeness.
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 complexity of validation tasks and the lack of annotations and output schema, the description is incomplete. It does not explain what validation entails, what the output looks like (e.g., success/failure, error messages), or how to interpret results. For a tool with no structured behavioral or output information, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools 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, documenting that 'params' is a free-form object passed as query string or JSON body. The description does not add any parameter-specific details beyond this schema, such as expected fields or examples for validation inputs. With high schema coverage, the baseline score is 3, as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: 'Validate Korean BRN (사업자등록번호), corp registration, phones'. It specifies the verb ('validate') and the resources (Korean business registration numbers, corporate registrations, phones), making the intent unambiguous. However, it does not differentiate this tool from potential sibling tools like 'email_validator' or 'json_schema_validator', which also perform validation tasks, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 specific contexts, prerequisites, or exclusions for validation, nor does it refer to sibling tools like 'k_company_lookup' or 'k_public_data' that might handle related data. Without such guidance, users must 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. It only states what the tool does ('lookup'), without mentioning whether it's read-only or mutative, authentication requirements, rate limits, error handling, or output format. For a lookup tool with zero annotation coverage, 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.
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 functionality. There's no wasted text, and it directly communicates the tool's purpose without unnecessary elaboration.
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 no annotations, no output schema, and a single but nested parameter, the description is incomplete. It doesn't cover behavioral aspects like safety, authentication, or rate limits, nor does it hint at return values or error conditions. For a lookup tool in a data-rich context, more guidance is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter ('params') documented as a free-form object for query strings or JSON bodies. The description adds value by specifying the key input ('business registration number'), but doesn't explain parameter structure, required fields, or format details beyond what the schema implies. 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: 'Korean company lookup by business registration number'. It specifies the action (lookup), resource (Korean company), and key parameter (business registration number). However, it doesn't differentiate from sibling tools like 'k_business_validator' or 'k_public_data', 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. It doesn't mention sibling tools like 'k_business_validator' or 'k_public_data', nor does it specify prerequisites, exclusions, or ideal use cases. The agent must infer usage from the name and description 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 'real-time + history' which hints at data retrieval behavior, but doesn't specify whether this is a read-only operation, what data sources are used, rate limits, authentication requirements, error conditions, or response format. For a data retrieval 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - just 8 words that efficiently convey the core functionality. It's front-loaded with the key data types (KRW exchange rate, BOK base rate, CPI) and temporal scope. However, it could be slightly more structured by separating the three data types more clearly.
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 financial data tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what format the data returns in, how historical data is accessed, what timeframes are available, or how real-time data is updated. The combination of missing behavioral context and output information creates significant gaps for an AI agent trying to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting that 'params' is a free-form object passed as query string for GET or JSON body for POST. The description doesn't add any parameter-specific information beyond what's in the schema, such as what specific parameters control (e.g., date ranges, currency pairs, data types). 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 provides KRW exchange rate, BOK base rate, and CPI data in both real-time and historical formats. It specifies the resource (exchange rate, base rate, CPI) and scope (real-time + history), but doesn't distinguish from siblings like 'k_market_intel' or 'k_public_data' that might offer related financial 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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention when this tool is appropriate versus sibling tools like 'k_market_intel' or 'ai_cost_calculator' that might handle related financial calculations, nor does it specify any prerequisites or exclusions 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 the full burden of behavioral disclosure. It only states what data is available, without mentioning how it's accessed (e.g., API calls, rate limits, authentication needs), what format the output is in, or any constraints like data freshness or availability. This is inadequate for a tool with a complex parameter structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single phrase listing domains—with zero wasted words. It's front-loaded and efficiently communicates the core purpose, though this brevity contributes to gaps in other dimensions.
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 complexity implied by multiple data domains, no annotations, no output schema, and a nested input schema with undefined parameters, the description is incomplete. It doesn't help the agent understand how to invoke the tool effectively or what to expect in return, making it inadequate for reliable 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%, but the schema only describes a generic 'params' object without specifics. The description adds no parameter semantics—it doesn't explain what parameters are needed for different data types (e.g., location for weather, date for holidays). This leaves the agent guessing about how to structure queries.
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 provides Korean government data in English across multiple domains (weather, FX, holidays, transport), which is specific about the resource and scope. However, it doesn't explicitly differentiate from sibling tools like k_exchange_rate or k_market_intel, which appear related to Korean data or markets.
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 lists domains but doesn't specify use cases, prerequisites, or exclusions. For example, it doesn't clarify if this should be used over k_exchange_rate for FX data or how it differs from general data 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 the full burden of behavioral disclosure. It mentions 'real-time pricing,' implying dynamic data, but doesn't cover critical aspects like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, 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. Every element ('50+ LLM models,' 'real-time pricing per token') earns its place by conveying essential information concisely.
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 no annotations and no output schema, the description is incomplete for a tool that likely returns complex pricing data. It lacks details on response structure, error conditions, or behavioral traits like data freshness. This makes it inadequate for an agent to use the tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter documented as a 'Free-form params object.' The description adds no additional parameter details beyond what the schema provides, such as example queries or specific filtering options. 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 provides pricing information for LLM models, specifying '50+ LLM models with real-time pricing per token.' It distinguishes itself from siblings like ai_cost_calculator by focusing on real-time pricing data rather than cost calculation. However, it doesn't explicitly mention the verb (e.g., 'retrieve' or 'fetch'), 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 siblings like ai_cost_calculator or llm_router, nor does it specify use cases, prerequisites, or exclusions. The agent must 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 full burden. It mentions 'auto-pick' behavior and quality/price criteria, but doesn't disclose important behavioral traits: what happens when no LLM meets the quality bar, whether it makes actual API calls or just recommends, what authentication is needed, rate limits, error handling, or response format. For a routing 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that immediately conveys the core functionality. Every word earns its place: 'Auto-pick' (action), 'cheapest LLM' (criteria), '(GPT-4 / Claude / Haiku)' (examples), 'that meets your quality bar' (constraint). No wasted words or unnecessary elaboration.
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 complexity of an LLM routing tool with no annotations and no output schema, the description is incomplete. It doesn't explain how quality is determined, what the output looks like, error conditions, or integration details. For a tool that presumably makes routing decisions between multiple AI providers, more context about behavior and results is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting a single 'params' object that accepts free-form parameters. The description adds no parameter-specific information beyond what's in the schema. Since schema coverage is high (>80%), the baseline score of 3 is appropriate - the description doesn't compensate but doesn't need to given the schema documentation.
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: 'Auto-pick cheapest LLM (GPT-4 / Claude / Haiku) that meets your quality bar'. It specifies the action (auto-pick), resources (LLMs), and criteria (cheapest, meets quality bar). However, it doesn't explicitly differentiate from sibling tools like 'llm_pricing' or 'ai_cost_calculator', 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 related to AI/LLM operations (llm_pricing, ai_cost_calculator, ai_provider_status, etc.), there's no indication of when this routing tool is appropriate versus those other tools for cost, status, or other LLM-related tasks.
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 lists operations but doesn't disclose behavioral traits like whether these are read-only or mutating operations, authentication needs, rate limits, or output formats. For a tool with potentially complex text processing, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—four brief phrases separated by commas—with zero wasted words. It's front-loaded with the core functions and efficiently communicates the scope in minimal space.
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 no annotations, no output schema, and a generic input schema, the description is incomplete. It lists functions but doesn't explain how they work, what inputs they expect, or what outputs they produce. For a tool with potentially multiple operations and complex text processing, this leaves too much undefined.
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 1 parameter with 100% description coverage, but the schema description is generic ('Free-form params object'). The tool description doesn't add any parameter-specific semantics, but with 0 required parameters and high schema coverage, the baseline is 3. Since it's a single free-form parameter, a score of 4 reflects that minimal parameter documentation is acceptable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists four functions (Markdown ↔ HTML, TOC extraction, link extraction, linting) which gives a general idea of capabilities, but it's vague about the specific verb+resource combinations and doesn't distinguish this tool from potential siblings. It mentions multiple operations without clarifying if it's a single multi-function tool or separate 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?
No guidance is provided on when to use this tool versus alternatives. The description lists functions but doesn't specify contexts, prerequisites, or exclusions. Given the sibling tools include text_analysis and other text-processing tools, this lack of differentiation is a significant gap.
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's function but lacks behavioral details such as input/output format, performance characteristics, rate limits, or error handling. This is inadequate for a tool with a free-form params object 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 a single, efficient phrase that front-loads the core purpose without unnecessary words. Every part earns its place by concisely stating the tool's function.
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 complexity (security detection tool), lack of annotations, no output schema, and a free-form params object, the description is incomplete. It fails to provide sufficient context for safe and effective use, such as expected input structure or result interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools 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, documenting a free-form params object. The description does not add meaning beyond the schema (e.g., example parameters or constraints), so it meets the baseline of 3 for high schema coverage without compensating value.
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 detecting prompt injection, jailbreak attempts, and PII in LLM inputs. It uses specific verbs ('detection') and identifies the target resource ('LLM inputs'), but does not differentiate from sibling tools, which appear unrelated (e.g., ai_budget_planner, email_validator).
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 implies usage for security/validation of LLM inputs, but lacks explicit context, prerequisites, or comparisons to other tools (e.g., text_analysis or other validation 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 the full burden of behavioral disclosure. It mentions the tool generates QR codes but fails to describe how it behaves—such as whether it makes network calls, has rate limits, requires authentication, or handles errors. This leaves significant gaps in understanding the tool's operational 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 extremely concise and front-loaded, packing essential information into a single, efficient sentence. Every word earns its place by specifying the action, outputs, and content types without redundancy or unnecessary elaboration.
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 (generating QR codes with various formats and content types), no annotations, and no output schema, the description is incomplete. It lacks details on behavior, error handling, or return values, making it inadequate for an agent to fully understand how to invoke and interpret results from this 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, documenting the 'params' object as free-form for GET/POST usage. The description adds no parameter-specific details beyond what the schema provides, such as example parameters or format requirements. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 function ('Generate QR codes') and specifies the output formats (PNG/SVG) and supported content types (URLs, vCards, WiFi, email, SMS). However, it doesn't differentiate from sibling tools, which are unrelated to QR code generation, making the distinction unnecessary rather than explicit.
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 other QR code generators or image creation tools. It lacks context about prerequisites, limitations, or typical use cases, leaving the agent to infer usage based solely on the stated purpose.
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 auditing but doesn't specify if this is a read-only analysis, requires authentication, has rate limits, or what the output format might be. For a tool with no annotations, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, using a single phrase that efficiently conveys the core function and key elements analyzed. Every word earns its place, with no wasted text or redundancy.
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 (SEO analysis with a free-form parameter object) and lack of annotations and output schema, the description is incomplete. It doesn't explain what the audit entails, how results are returned, or any behavioral traits, making it inadequate for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, indicating the single parameter is documented as a 'Free-form params object.' The description adds no additional meaning about parameters beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.
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 function as a 'Page-level SEO audit' and lists specific elements analyzed (meta tags, headings, links, alt text), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools, which are unrelated to SEO analysis, so it doesn't need sibling differentiation but could be more specific about 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?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or context. It merely states what the tool does without indicating appropriate scenarios 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool scans for vulnerabilities but doesn't describe how it works (e.g., static analysis, simulation), what inputs it expects (beyond the vague 'params'), or any limitations (e.g., false positives, supported Solidity features). This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single phrase listing the tool's core function and key vulnerability types. It's front-loaded with essential information and wastes no words, making it efficient for quick comprehension.
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 complexity of smart contract scanning (a non-trivial task with 1 parameter and no output schema), the description is insufficient. It lacks details on input format (e.g., Solidity code structure), output expectations, error handling, or any behavioral context. Without annotations or an output schema, this leaves the agent poorly equipped to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting that 'params' is a free-form object passed as query string or JSON body. The description adds no parameter-specific information beyond what the schema provides, such as expected fields or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: scanning Solidity code for specific vulnerabilities (reentrancy, overflow, access control). It uses a specific verb ('scan') and resource ('Solidity'), making the function unambiguous. However, it doesn't differentiate from sibling tools, as none appear to be related to smart contract analysis, so it doesn't need explicit sibling 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?
The description provides no guidance on when to use this tool versus alternatives. It lists vulnerability types but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't indicate if it's for production code, testing, or specific Solidity versions, leaving usage unclear.
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 what the tool does (detects technologies) but provides no information about authentication requirements, rate limits, response format, error conditions, or whether it performs external network calls. For a tool that presumably makes HTTP requests to analyze URLs, this lack of behavioral context is significant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just one sentence that efficiently communicates the core functionality ('Detect 150+ technologies on any URL') and provides a helpful comparison point ('BuiltWith alternative'). Every word earns its place, and the information is front-loaded with the primary purpose stated first.
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 (detecting 150+ technologies via URL analysis), lack of annotations, no output schema, and a single but complex parameter (free-form object), the description is insufficient. It doesn't explain what technologies are detected, how results are returned, what the 'params' object should contain, or any limitations. The description should provide more context for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting that it accepts a 'params' object with free-form parameters. The description adds no parameter-specific information beyond what the schema already provides. With high schema coverage and no additional parameter semantics in the description, 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: 'Detect 150+ technologies on any URL' with a specific verb ('detect') and resource ('technologies on any URL'). It distinguishes itself from siblings by focusing on technology detection rather than AI, crypto, data, or other categories. However, it doesn't explicitly differentiate from potential similar tools like 'seo_analyzer' or 'smart_contract_scanner' within the same server.
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 'BuiltWith alternative' as a comparison point, but doesn't explain when this tool is preferable over other technology detection methods or related sibling tools like 'seo_analyzer' or 'smart_contract_scanner'. There are no explicit when/when-not instructions or alternative recommendations.
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, the description carries the full burden of behavioral disclosure. It mentions performance ('sub-10ms redirects') and analytics features (click tracking, geo stats), but lacks details on permissions, rate limits, error handling, or output format, which are critical for a tool that modifies URLs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, using a single sentence with no wasted words. Every phrase ('Shorten URLs', 'click tracking', 'geo stats', 'sub-10ms redirects') adds value by specifying functionality.
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 complexity of a URL shortener with analytics and no annotations or output schema, the description is incomplete. It omits details on required inputs, return values, error cases, and operational constraints, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema documents the single parameter ('params' as a free-form object). The description adds no parameter-specific information beyond what's in the schema, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 function ('Shorten URLs') and mentions key features (click tracking, geo stats, sub-10ms redirects). It's specific about the verb and resource, but doesn't explicitly differentiate from sibling tools, which are unrelated to URL shortening.
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 lists features but doesn't indicate scenarios, prerequisites, or comparisons with other URL shorteners or 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 'capture, store, and replay' which implies stateful operations and persistence, but doesn't disclose critical behavioral traits like storage duration, replay mechanics, authentication needs, rate limits, or error handling. For a tool with potential side effects, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (8 words) and front-loaded with all essential information. Every word earns its place: 'Capture, store, and replay' defines operations, 'webhook payloads' specifies the resource, and 'debug integrations' states the purpose. No wasted verbiage.
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 no annotations, no output schema, and a single but complex parameter (nested object), the description is incomplete. It doesn't explain what 'capture' entails (e.g., how webhooks are registered), how storage works, replay mechanics, or what the tool returns. For a stateful debugging tool, this leaves too many unknowns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'params' documented as a free-form object for query strings (GET) or JSON body (POST). The description adds no additional parameter semantics beyond what the schema provides. Since schema coverage is high, baseline 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 with specific verbs ('capture, store, and replay') and resource ('webhook payloads'), plus the goal ('debug integrations'). It distinguishes from sibling tools by focusing on webhook debugging rather than AI, data, or other utilities. However, it doesn't explicitly differentiate from hypothetical similar webhook 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. It mentions 'debug integrations' as a use case but doesn't specify prerequisites, constraints, or when other tools might be more appropriate. With many sibling tools available, this lack of comparative context is a significant gap.
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 lists output fields but doesn't describe how the tool behaves: e.g., whether it requires authentication, rate limits, error handling, or data freshness. For a tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, using a compact 'IP →' notation followed by a comma-separated list of output fields. Every element (arrow symbol, field names) earns its place by conveying core functionality without waste. It's 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 low complexity (single parameter, no output schema, no annotations), the description is somewhat complete but lacks depth. It covers the basic purpose and output fields but misses behavioral details (e.g., how to structure inputs, error cases). For a tool with minimal structured data, it should provide more context to compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools 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, documenting a single 'params' object as free-form parameters passed in queries or JSON. The description adds no parameter-specific information beyond what the schema provides, such as expected keys (e.g., 'ip') or formats. 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 function: converting an IP address to geographic and network information (country, city, coordinates, timezone, ISP, ASN). It uses a concise 'IP →' format that implies transformation. However, it doesn't explicitly differentiate from sibling tools like 'k_address_geocoder' or 'timezone', which might offer 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 any prerequisites, limitations, or comparisons to sibling tools like 'k_address_geocoder' (which might handle addresses instead of IPs) or 'timezone' (which might provide timezone data independently). Usage context is implied but not explicit.
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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