Server Quality Checklist

Tool Scores

• requestA
  3.7/5.0
  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: automatic login handling, token expiration management, and the response structure including 'login_data' with IDs like 'pharmacyId'. It covers authentication flow and output format, though it doesn't mention error handling, rate limits, or side effects like data modification for write methods.

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

  Conciseness5/5

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

  The description is highly concise and well-structured in two sentences. The first sentence states the core purpose and key behavior (authenticated API call with auto-login). The second sentence details the return value. Every sentence adds essential information without redundancy, making it efficient and front-loaded.

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

  Completeness3/5

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

  Given the tool's complexity (handles authentication, multiple HTTP methods, and returns structured data) and lack of annotations and output schema, the description is moderately complete. It covers authentication behavior and response format but omits details on error handling, side effects for write operations, and how it differs from sibling tools. For a general-purpose API tool, more context on safety and usage boundaries 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 5 parameters thoroughly. The description adds minimal parameter-specific semantics, only implying that 'body' is for POST/PUT/PATCH methods and 'skip_auth' bypasses Authorization headers. This provides some context but doesn't significantly enhance understanding beyond the schema's detailed descriptions.

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

  Purpose4/5

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

  The description clearly states the tool's purpose: 'Makes an authenticated API call' with automatic login handling. It specifies the verb ('makes') and resource ('authenticated API call'), distinguishing it from generic HTTP tools by mentioning authentication. However, it doesn't explicitly differentiate from sibling tools like 'fetch_spec' or 'search_endpoints', which might also involve API interactions.

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

  Usage Guidelines3/5

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

  The description implies usage for authenticated API calls with automatic token renewal, suggesting it's for endpoints requiring auth. It mentions 'skip_auth' for public endpoints, providing some context. However, it lacks explicit guidance on when to use this tool versus siblings like 'fetch_spec' or 'search_endpoints', and doesn't specify prerequisites or exclusions beyond auth handling.

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

• fetch_specA
  4.2/5.0
  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 describes the tool's behavior (fetching a spec for discovery), but lacks details on error handling, rate limits, or authentication needs. It adds some context about the default URL source but doesn't fully compensate for the lack of annotations.

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

  Conciseness5/5

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

  The description is front-loaded with the core purpose, uses two concise sentences with zero waste, and efficiently communicates key usage information without unnecessary details.

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

  Completeness4/5

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

  Given the tool's low complexity (1 optional parameter, no output schema), the description is mostly complete for its purpose. However, it could benefit from mentioning the output format (JSON) or potential errors, slightly limiting completeness for a discovery tool.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents the single parameter. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or usage nuances, meeting the baseline for high schema coverage.

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

  Purpose5/5

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

  The description clearly states the specific action ('Fetches') and resource ('OpenAPI/Swagger JSON spec for this API'), and distinguishes it from sibling tools by explicitly mentioning its role in discovering endpoint information before using the 'request()' tool.

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

  Usage Guidelines5/5

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

  It provides explicit guidance on when to use this tool ('before calling request()') and implies an alternative (using 'request()' directly), with clear context about its purpose for API discovery.

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

• search_endpointsA
  4.2/5.0
  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 describes the search behavior ('fuzzy-search'), scope ('searches across path, HTTP method...'), and return format ('Returns matching endpoints with their method, full path...'). However, it doesn't mention performance characteristics, rate limits, authentication needs, or error handling, which are gaps for a search tool.

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

  Conciseness5/5

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

  The description is perfectly concise and well-structured in two sentences. The first sentence states the purpose and usage guidance, while the second explains search scope and return format. Every word earns its place with zero waste 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 (search functionality with 2 parameters), no annotations, and no output schema, the description does a good job covering purpose, usage, behavior, and returns. However, it lacks details on output structure (beyond listing fields) and error cases, leaving some gaps for the agent to infer.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain search algorithm details or result ordering). This meets the baseline of 3 when the schema does the heavy lifting.

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

  Purpose5/5

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

  The description clearly states the tool's purpose with a specific verb ('fuzzy-search') and resource ('API spec'), and distinguishes it from siblings by explaining it's for when you don't know the exact path. It explicitly mentions what gets searched (path, HTTP method, summary, etc.) and what's returned (matching endpoints with method, path, summary, parameters).

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

  Usage Guidelines5/5

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

  The description provides explicit usage guidance: 'Use this when you don't know the exact path.' This clearly indicates when to choose this tool over alternatives like fetch_spec (which presumably fetches the full spec) or request (which makes actual API calls). The context is well-defined with no ambiguity.

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