Server Quality Checklist

Tool Scores

• submit_feedbackB
  3.3/5.0
  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 tool 'helps improve the developer experience' but doesn't disclose behavioral traits like whether submission is anonymous, requires authentication, has rate limits, or provides confirmation. For a feedback tool with zero annotation coverage, this leaves significant gaps in understanding how it operates beyond the basic action.

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

  Conciseness5/5

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

  The description is two concise sentences with zero waste: the first specifies the action and scope, and the second states the purpose. It's front-loaded with the core function and appropriately sized, making it easy to scan and understand quickly.

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

  Completeness3/5

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

  Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and general usage but lacks details on behavioral aspects like response handling or error conditions. Without annotations or output schema, more context on what happens after submission would enhance completeness for a feedback 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 fully documents both parameters ('feedback' and 'tool_name') with descriptions and constraints. The description adds no additional meaning beyond what the schema provides, such as examples of feedback content or how 'tool_name' relates to sibling tools. Baseline 3 is appropriate when schema does the heavy lifting.

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

  Purpose4/5

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

  The description clearly states the action ('submit feedback') and target ('to Ramp about the MCP server interface, tools, or problems'), with a specific purpose ('Helps improve the developer experience'). It distinguishes from siblings like 'get_endpoint_schema' or 'search_documentation' by focusing on feedback submission rather than information retrieval. However, it doesn't explicitly contrast with 'ping' (which tests connectivity), leaving minor ambiguity.

  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 feedback on 'MCP server interface, tools, or problems,' providing general context. It doesn't specify when NOT to use it (e.g., for technical support vs. bug reports) or name explicit alternatives among siblings. Guidelines are present but lack detailed exclusions or comparative advice, relying on implied understanding.

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

• pingA
  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 discloses the behavioral trait of testing connectivity, which is useful context. However, it lacks details such as expected response format, potential errors, or any side effects, leaving gaps in behavioral understanding.

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

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose with zero wasted words. Every part of the sentence directly contributes to understanding 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.

  Completeness4/5

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

  Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is mostly complete for its purpose. However, it could slightly improve by hinting at the expected outcome (e.g., success/failure response) to fully cover behavioral aspects, though this is a minor gap.

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

  Parameters4/5

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

  The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately does not discuss parameters, earning a high baseline score for not adding unnecessary information.

  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 ('Test connectivity') and target resource ('to the MCP server'), distinguishing it from sibling tools like get_endpoint_schema or search_documentation. It uses precise language that directly communicates the tool's function without ambiguity.

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

  Usage Guidelines4/5

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

  The description implies usage context ('Test connectivity') suggesting it should be used for diagnostic or verification purposes. However, it does not explicitly state when not to use it or name specific alternatives, which prevents a perfect score despite the clear implied context.

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

• search_documentationA
  4.3/5.0
  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 key behavioral traits: it processes natural language queries, searches documentation, reasons about relevance, and returns markdown chunks. However, it lacks details on limitations (e.g., search scope, accuracy, rate limits) or output specifics (e.g., format details, error handling). For a tool with no annotations, this is adequate but not comprehensive.

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

  Conciseness4/5

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

  The description is appropriately sized and front-loaded with a clear purpose statement. Sections like 'What this does' and 'Perfect for' are well-structured, but some elements (e.g., emojis, bullet points) add minor visual clutter without essential information. Overall, it's efficient with minimal waste, though it could be slightly more streamlined.

  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 complexity (natural language search with reasoning) and lack of annotations/output schema, the description is reasonably complete. It covers purpose, usage, parameters, and behavioral aspects. However, it could improve by addressing potential limitations or output details more explicitly, which would enhance completeness for a tool with no structured support.

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

  Parameters4/5

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

  The input schema has 100% description coverage, with the query parameter well-documented. The description adds value by elaborating on the parameter's semantics: it emphasizes natural language usage, provides intent detection context, and gives concrete examples ('building an integration', 'OAuth setup'). This goes beyond the schema's basic description, though it doesn't introduce new parameter details.

  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 ('finds', 'searches', 'detects', 'reasons about') and resources ('Ramp API documentation', 'documentation and guides'). It distinguishes itself from sibling tools like get_endpoint_schema (which likely retrieves schema details) and ping/submit_feedback (which are unrelated to documentation search). The title 'SMART DOCUMENTATION SEARCH' reinforces this specificity.

  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 through the 'Perfect for' section, listing specific scenarios like authentication, API workflows, examples, and troubleshooting. It also distinguishes usage from alternatives by implying this is for natural language queries about documentation, unlike get_endpoint_schema which might handle structured schema retrieval. The 'Usage' section reinforces this with examples.

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

• get_endpoint_schemaA
  4.4/5.0
  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 does well by explaining what the tool returns (exact OpenAPI schema), what inputs are needed (endpoint path, optional method), and behavioral aspects like 'will show most relevant method' when method isn't specified. It doesn't mention rate limits or authentication requirements, but provides solid 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 well-structured with clear sections (purpose, use cases, examples, usage instructions), front-loaded with the core purpose, and every sentence adds value. No wasted words while maintaining comprehensive coverage.

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

  Completeness4/5

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

  For a tool with 3 parameters, 100% schema coverage, but no output schema, the description provides excellent context about what the tool returns and when to use it. The main gap is lack of information about return format or structure, which would be helpful since there's no output schema. However, it compensates well with usage examples and scenarios.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds some context about 'most relevant method' when method isn't specified, but doesn't provide significant additional parameter semantics beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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

  Purpose5/5

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

  The description clearly states the tool's purpose with specific verb ('Returns exact OpenAPI schema') and resource ('for specific endpoints'). It distinguishes itself from sibling tools like 'search_documentation' by focusing on precise technical specifications rather than general documentation search.

  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 through the 'Perfect for when you need' section listing specific scenarios, and includes 'Example queries this replaces' showing concrete alternatives. It clearly differentiates this tool's use case from general documentation search.

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