Server Quality Checklist

Tool Scores

• figma_designC
  2.9/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data but lacks details on permissions, rate limits, error handling, or what 'structure and images' entails (e.g., format, size). This is a significant gap for a tool with no annotation coverage.

  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 with zero waste. It is front-loaded with the core purpose and includes essential details ('structure and images') without redundancy. Every word earns its place, 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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'Figma design data' includes beyond 'structure and images', how results are returned, or behavioral aspects like authentication needs. For a data retrieval tool with rich context (Figma API), more detail is warranted.

  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 single parameter 'url' fully documented in the schema as 'The full Figma design URL'. The description adds no additional meaning beyond this, such as URL format examples or constraints. Baseline 3 is appropriate 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.

  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 a specific verb ('Get') and resource ('Figma design data'), specifying what data is retrieved ('structure and images'). It distinguishes from sibling tools like 'generate_image' (creation vs retrieval) and 'mcp_fetch' (generic vs Figma-specific), though it doesn't explicitly mention these distinctions.

  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 (e.g., needing a valid Figma URL), exclusions, or comparisons to siblings like 'mcp_fetch' for general fetching or 'generate_image' for image creation. Usage is implied only by the purpose statement.

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

• generate_imageC
  2.9/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('generate') but doesn't disclose traits like whether it's a read-only or destructive operation, authentication needs, rate limits, response format, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 with zero waste. It front-loads the core purpose ('Generate an image') and specifies the method ('using DALL-E 3'), making it easy to understand quickly without unnecessary details.

  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 (image generation with 4 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., image URL, binary data), error conditions, or behavioral traits. For a tool with no structured support, 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?

  Schema description coverage is 100%, so the schema fully documents all parameters (prompt, size, quality, n). The description adds no additional meaning beyond what the schema provides, such as examples or usage tips. Baseline score of 3 is appropriate since the schema handles parameter documentation effectively.

  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 verb 'generate' and the resource 'image', specifying it uses DALL-E 3. This distinguishes it from sibling tools like figma_design, mcp_fetch, and mood, which don't involve image generation. However, it doesn't explicitly mention what type of images (e.g., AI-generated, artistic) or differentiate further from potential unseen 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 doesn't mention any prerequisites, constraints (e.g., rate limits, costs), or compare it to sibling tools. Usage is implied only by the tool's name and description, with no explicit context or exclusions provided.

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

• mcp_fetchC
  2.9/5.0
  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 fetching and returning content but doesn't cover important traits like error handling, network timeouts, authentication needs, rate limits, or what 'content' entails (e.g., HTML, text). This leaves significant gaps for an agent.

  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 with zero waste. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

  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 (fetching websites can involve network issues, varied content types) and lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error cases, or behavioral nuances, leaving the agent with insufficient information.

  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% coverage with a clear description for the 'url' parameter. The description adds no additional meaning beyond the schema, such as URL format constraints or examples. Baseline 3 is appropriate 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.

  Purpose4/5

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

  The description clearly states the verb ('fetches') and resource ('a website'), specifying what the tool does. However, it doesn't differentiate from potential siblings like 'figma_design' or 'generate_image', which are unrelated, so it lacks explicit 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 or in what context it's appropriate. It simply states the action without any usage context, prerequisites, or exclusions.

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

• moodA
  3.5/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries full burden. It discloses key behavioral traits: the server will 'always respond with a cheerful message and a heart ❤️' (as noted in the input schema description, which complements the tool description). However, it doesn't cover other aspects like response format, error handling, or performance characteristics. The description adds some context but isn't comprehensive.

  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 (one sentence) and front-loaded with the core purpose. Every word earns its place: 'Ask the server about its mood' states the action, and 'it's always happy!' adds useful behavioral context without redundancy. No unnecessary details or fluff are present.

  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 has gaps. It covers the purpose and basic behavior, but lacks details on error cases, response structure, or integration with sibling tools. Without an output schema, more explanation of return values would be helpful, though the input schema hints at the response.

  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 input schema providing detailed semantics for the 'question' parameter, including examples and expected server behavior. The tool description adds no additional parameter information beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info 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: 'Ask the server about its mood' with the specific action 'ask' and resource 'server mood'. It distinguishes from siblings like 'figma_design' or 'generate_image' by focusing on conversational interaction rather than design or image generation. However, it doesn't explicitly contrast with 'mcp_fetch', which might also involve server communication.

  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 'it's always happy!', suggesting this is for casual interaction rather than functional tasks. However, it lacks explicit guidance on when to use this versus alternatives like 'mcp_fetch' or other tools, and doesn't specify prerequisites or exclusions. The input schema provides example questions, but the description itself offers only implied usage.

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