Server Quality Checklist

Tool Scores

• list_top_modelsA
  3.8/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about the output format ('compact Markdown table ~250 tokens') and the ranking focus, but doesn't cover other behavioral aspects like rate limits, data freshness, or error handling. The description doesn't contradict any annotations, but could be more comprehensive given the lack of structured annotations.

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

  Conciseness5/5

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

  The description is extremely concise (two sentences) and front-loaded with the core purpose. Every sentence earns its place: the first defines the action and categories, the second specifies the output format. No wasted words or redundant information.

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

  Completeness4/5

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

  Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is reasonably complete. It covers the purpose, categories, and output format, though it could benefit from more behavioral context. Without an output schema, the description helpfully specifies the return format, but additional details about ranking criteria or data sources would improve completeness.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by listing the category options and implying the ranking logic, but doesn't provide additional semantic context beyond what's in the schema. With high schema coverage, the baseline score of 3 is appropriate.

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

  Purpose5/5

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

  The description clearly states the specific action ('List top-ranked LLM/VLM models'), the resource ('models'), and the scope ('for a category'). It distinguishes from sibling tools like 'compare_models', 'get_model_info', and 'recommend_model' by focusing on ranking and tabular output rather than comparison, detailed info, or personalized recommendations.

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

  Usage Guidelines3/5

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

  The description implies usage context by listing categories and mentioning the output format, but it doesn't explicitly state when to use this tool versus alternatives like 'compare_models' or 'recommend_model'. No exclusions or prerequisites are provided, leaving the agent to infer appropriate usage scenarios.

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

• compare_modelsA
  4/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool performs comparison (not creation or modification), returns a Markdown table with specific content areas, and has a token limit (~400 tokens). However, it doesn't mention potential limitations like data freshness, error handling, or authentication needs, leaving some gaps.

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

  Conciseness5/5

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

  The description is extremely concise and front-loaded, with every sentence earning its place: the first defines the core functionality, and the second specifies the output format and constraints. There's no wasted text or redundancy.

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

  Completeness4/5

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

  Given the tool's moderate complexity (comparing models across multiple dimensions) and lack of annotations/output schema, the description is mostly complete. It covers purpose, scope, and output format but could benefit from mentioning data sources or update frequency. Without an output schema, it helpfully describes the return format (Markdown table).

  Complex tools with many parameters or behaviors need more documentation. Simple tools 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 'models' parameter fully documented in the schema (array of strings, 2-5 items, example values). The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 without compensating or adding 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 ('compare') and resources ('2-5 LLM/VLM models'), specifying what aspects are compared (pricing, benchmarks, capabilities) and the output format (compact Markdown comparison table). It distinguishes from siblings like 'get_model_info' (single model details), 'list_top_models' (ranking), and 'recommend_model' (suggestion).

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

  Usage Guidelines4/5

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

  The description provides clear context for when to use this tool (comparing multiple models side-by-side) and implies usage by specifying the 2-5 model range. However, it doesn't explicitly state when not to use it or name alternatives like 'get_model_info' for single-model details, which would be needed for a perfect score.

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

• get_model_infoA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It discloses the output format (structured Markdown) and approximate size (~300 tokens), which is useful behavioral context. However, it lacks details on error handling, rate limits, authentication needs, or whether the operation is read-only (implied by 'Get' but not explicit).

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

  Conciseness5/5

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

  The description is a single, well-structured sentence that efficiently conveys the tool's purpose, content, and output format without unnecessary words. It is front-loaded with the core action and resource, making it easy to scan and understand.

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

  Completeness4/5

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

  Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is reasonably complete. It covers the purpose, output format, and content, but could improve by addressing behavioral aspects like error cases or usage guidelines relative to siblings. The lack of output schema means the description should ideally hint at return structure, which it does with 'structured Markdown (~300 tokens)'.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema fully documents the parameters. The description adds minimal value beyond the schema by mentioning 'pricing, benchmarks, capabilities, and ready-to-use API code example', which loosely relates to parameters but does not provide additional syntax or format details. Baseline 3 is appropriate as the schema handles the heavy lifting.

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

  Purpose5/5

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

  The description clearly states the verb 'Get' and the resource 'detailed information about a specific LLM/VLM model', specifying the content (pricing, benchmarks, capabilities, API code example) and output format (structured Markdown). It distinguishes from sibling tools like compare_models, list_top_models, and recommend_model by focusing on a single model's details rather than comparison, listing, or recommendation.

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

  Usage Guidelines4/5

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

  The description implies usage when detailed information about a specific model is needed, with context about what information is returned. However, it does not explicitly state when to use this tool versus alternatives like compare_models for comparisons or list_top_models for overviews, nor does it provide exclusions or prerequisites.

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

• recommend_modelA
  4/5.0
  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 discloses key behavioral traits: it specifies the output format ('Returns top 3 picks with reasoning') and approximate length ('~350 tokens'), though it lacks details on rate limits, error handling, or data sources.

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

  Conciseness5/5

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

  The description is appropriately sized and front-loaded, with two concise sentences that efficiently convey the tool's purpose and output without any wasted words or redundancy.

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

  Completeness4/5

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

  Given the tool's moderate complexity (8 parameters, no output schema, no annotations), the description is reasonably complete: it covers purpose and output format, but could benefit from more behavioral context (e.g., data sources, limitations) to fully compensate for the lack of annotations and output schema.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema fully documents all 8 parameters. The description adds minimal value by mentioning 'use case, budget, and requirements,' which loosely maps to some parameters but does not provide additional semantics beyond what the schema already specifies.

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

  Purpose5/5

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

  The description clearly states the tool's purpose with a specific verb ('Get personalized model recommendations') and resource ('model recommendations'), and distinguishes it from siblings by focusing on personalized recommendations rather than comparison, listing, or general information retrieval.

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

  Usage Guidelines3/5

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

  The description implies usage context through 'based on use case, budget, and requirements,' but does not explicitly state when to use this tool versus alternatives like compare_models or list_top_models, nor does it provide exclusions or prerequisites.

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