Server Quality Checklist

Tool Scores

• get_statusB
  3.4/5.0
  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 of behavioral disclosure. It states the tool returns server status information, implying a read-only operation, but does not explicitly confirm safety (e.g., non-destructive), rate limits, authentication needs, or error handling. The description adds basic context about what information is returned, but lacks depth on behavioral traits beyond the core functionality.

  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: it starts with the purpose, then details the return content in a structured list. The use of markdown-like formatting (e.g., '---' and bullet points) enhances readability. However, the inclusion of '返回内容' (return content) details might be redundant if an output schema exists (which it does), slightly reducing efficiency.

  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 (0 parameters, simple read operation) and the presence of an output schema (which should cover return values), the description is reasonably complete. It explains the purpose and outlines return fields, which is sufficient for a status-checking tool. However, it lacks behavioral details like error cases or performance considerations, preventing a perfect score.

  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 the input schema has 100% description coverage (though empty). The description does not need to compensate for any parameter gaps. It appropriately focuses on the output, listing return fields like server_name, provider, model, etc., which adds value beyond the input schema. This meets the baseline for zero parameters, but does not go above by explaining parameter interactions or constraints (since there are none).

  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: '获取服务器状态' (get server status) and elaborates with '返回当前服务器配置和运行状态信息' (returns current server configuration and running status information). This specifies the verb ('get'/'return') and resource ('server status'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools (analyze_image, chat_vision), which serve entirely different purposes, so it falls short of a perfect score.

  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 does not mention sibling tools or any contextual cues for selection. The only implied usage is to retrieve server status, but there is no explicit when/when-not advice or prerequisites, leaving the agent to infer usage based solely on the purpose statement.

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

• analyze_imageA
  3.9/5.0
  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 mentions the tool returns detailed descriptions and lists specific use cases, which helps understand its behavior. However, it doesn't disclose important traits like rate limits, authentication requirements, error handling, or whether it's read-only vs. destructive. The description adds some context but leaves significant behavioral 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 well-structured with clear sections (purpose, usage scenarios, parameter explanation, examples, return content) and every sentence adds value. It's appropriately sized for a tool with 2 parameters and comprehensive examples, with 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 has 2 parameters with 100% schema coverage and an output schema (implied by the '返回内容' section), the description provides good contextual completeness. It covers purpose, usage scenarios, parameter semantics with examples, and return values. The main gap is lack of behavioral transparency details that would be important for a tool performing image analysis.

  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 schema description coverage is 100%, so the schema already documents both parameters well. The description adds value by providing concrete examples of parameter usage in different scenarios (basic description, OCR, code recognition) and clarifies that 'image' accepts both local file paths and Base64 encoding, which enhances understanding beyond the schema's basic 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 as '分析图像内容' (analyze image content) and specifies it returns detailed descriptions, which is a specific verb+resource combination. However, it doesn't explicitly distinguish this from sibling tools like 'chat_vision' or 'get_status', leaving some ambiguity about when to choose one over another.

  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 '使用场景' (usage scenarios) section provides clear contexts for when to use this tool: image content recognition, OCR, code screenshot recognition, data chart analysis, and technical chart understanding. This gives good guidance, but it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools.

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

• chat_visionA
  4.1/5.0
  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 and does so well by disclosing key behavioral traits: the two-round conversation limit ('超过两轮将无法继续'), session management requirements, and workflow constraints. It also describes the return structure and state tracking (e.g., 'remaining_turns', 'can_continue'), though it could mention potential error conditions or performance characteristics.

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

  Conciseness3/5

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

  The description is well-structured with clear sections (使用场景, 参数说明, 两轮对话流程, 示例, 返回内容), but it is verbose with repetitive information (e.g., the workflow is explained in multiple places). Some sentences could be condensed without losing clarity, making it less front-loaded than ideal.

  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 (multi-round conversation with state management), no annotations, and the presence of an output schema, the description is highly complete. It covers purpose, usage, parameters, workflow, examples, and return values, providing sufficient context for an agent to use the tool correctly without relying on structured fields.

  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%, so the baseline is 3. The description adds some value by explaining the conversational flow implications of parameters (e.g., 'session_id' for second-round dialogue, 'is_new_conversation' to create new sessions) and providing usage examples, but doesn't significantly enhance semantic understanding beyond what the schema already documents.

  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 performs 'two-round conversational image Q&A' with specific verbs ('analyze', 'answer') and resources ('image', 'question'), distinguishing it from sibling tools like 'analyze_image' by emphasizing the conversational aspect. However, it doesn't explicitly contrast with 'get_status', leaving some ambiguity in 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 Guidelines5/5

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

  The description provides explicit usage scenarios ('深度图像分析', '迭代式问题探索', '复杂图像理解'), detailed two-round workflow instructions, and clear when-to-use guidance (e.g., '首次调用可不提供' for session_id, '超过两轮将无法继续'). It effectively guides the agent on proper invocation timing and limitations.

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