Server Quality Checklist

Tool Scores

• get_modelA
  4.5/5.0
  Behavior4/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description carries full burden and does well: it discloses the 404 error behavior, mentions case-sensitivity (though partly in schema), and implies read-only operation through 'Get'. However, it doesn't mention rate limits, authentication needs, or response format details beyond content types.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Three sentences, each earning its place: first states purpose and return values, second provides usage context, third covers error case and alternative. No wasted words, front-loaded with core functionality.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's moderate complexity, 100% schema coverage, and presence of an output schema (which handles return values), the description is complete enough. It covers purpose, usage, error behavior, and sibling differentiation without needing to duplicate structured data.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, providing detailed parameter documentation. The description adds minimal value beyond schema, only reinforcing the format and discovery advice. Baseline 3 is appropriate when schema does heavy lifting.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the verb ('Get') and resource ('complete specs for a model by ID'), specifying what information is returned (pricing, context window, capabilities, architecture, per-request limits). It distinguishes from sibling 'find_models' by focusing on detailed specs rather than discovery.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicit guidance is provided: 'Call after find_models to get full details, or when the user provides a model ID.' It also specifies when not to use it ('Returns 404 if model not found. Use find_models to discover valid IDs.') and names the alternative tool ('find_models').

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• find_modelsA
  4.7/5.0
  Behavior4/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the scoring system (0-150+ with explanation of what influences scores), pagination support, and the semantic/fuzzy matching nature of the search. However, it doesn't mention rate limits, authentication requirements, or error conditions, which would be helpful for a search tool with 9 parameters.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is well-structured and efficiently organized. It starts with the core purpose, then provides usage guidance, parameter overview, scoring explanation, and workflow integration. Every sentence serves a clear purpose with no redundancy. The bullet-point style for parameters makes it scannable while maintaining completeness.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (9 parameters, search functionality), the description provides excellent context. It explains the discovery workflow, parameter interactions, scoring interpretation, and integration with sibling tools. With 100% schema coverage and an output schema present, the description focuses on the semantic understanding needed to use the tool effectively rather than repeating schema details.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so the baseline is 3. The description adds significant value by explaining parameter semantics beyond the schema: it clarifies that query uses 'semantic search with fuzzy matching,' explains the score field's purpose and interpretation, and provides practical examples for capabilities filtering ('AND logic'). However, it doesn't fully explain the interaction between query and filter parameters.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Search and filter 300+ AI models. Returns ranked results with pricing, context windows, and capabilities.' This specifies the exact action (search and filter), resource (AI models), and scope (300+ models with specific return fields). It distinguishes from siblings by explaining this is the discovery tool to get model IDs for use with get_model and test_model.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides explicit guidance: 'Call this tool first to discover model IDs, unless the user provides one.' It also states when to use alternatives: 'Use model IDs from results with get_model for full specs or test_model for live testing.' This gives clear context about the tool's role in the workflow and when to use sibling tools instead.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• test_modelA
  4.7/5.0
  Behavior4/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: authentication requirements ('Requires OPENROUTER_API_KEY'), billing implications ('Costs are billed to your OpenRouter account'), and performance characteristics ('Returns output text, latency (ms), token usage, and cost estimates'). It doesn't mention rate limits or error handling, keeping it from a perfect score.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is perfectly structured and concise: it starts with the core purpose, then covers requirements, parameters with practical guidance, and ends with sibling tool references. Every sentence earns its place with no redundancy or wasted words.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (6 parameters, live API calls, billing implications) and the presence of an output schema (which handles return values), the description is complete: it covers purpose, authentication, billing, parameter relationships, practical usage tips, and sibling tool coordination. Nothing essential appears missing.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  With 100% schema description coverage, the baseline is 3, but the description adds meaningful context beyond the schema: it explains the relationship between test_type and prompt ('prompt overrides test_type'), provides practical guidance on max_tokens usage ('Use 100-500 for quick tests'), and clarifies that all models receive identical prompts. This adds significant practical value.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose with specific verbs ('Make live API calls', 'Returns output text') and resources ('to 1-5 models via OpenRouter'). It distinguishes from siblings by mentioning 'Use find_models or get_model first to identify model IDs', showing awareness of complementary tools.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides explicit guidance on when to use this tool vs alternatives: 'Use find_models or get_model first to identify model IDs' indicates prerequisites and sibling relationships. It also explains parameter interactions ('prompt overrides test_type') and provides context about billing requirements.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.