Server Quality Checklist

Tool Scores

• knowledge_recallB
  3.1/5.0
  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 that results are '未经AI处理的原始结果' (raw results without AI processing), which is valuable behavioral context. However, it doesn't disclose other important traits: whether this is a read-only operation, what permissions are needed, rate limits, error conditions, or what the return format looks like (especially problematic since there's no output schema). For a 7-parameter tool with no annotation coverage, this is insufficient.

  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 concise with two sentences. The first sentence states the core purpose and key behavioral trait (raw results). The second sentence provides usage context. There's no wasted text, and the information is front-loaded. It could potentially be more structured with explicit sections, but it's efficient for its length.

  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 complexity (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (critical without output schema), doesn't mention error handling, and provides minimal behavioral context. While it covers the basic purpose and one key trait (raw results), it leaves too many gaps for a tool of this complexity with no structured support from annotations or 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?

  The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description. The description doesn't compensate or add value here.

  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: '在Get笔记知识库中召回相关内容' (recall related content in Get笔记 knowledge base) with the specific verb '召回' (recall/retrieve) and resource '相关内容' (related content). It distinguishes from sibling 'knowledge_search' by specifying '返回未经AI处理的原始结果' (returns raw results without AI processing). However, it doesn't explicitly contrast with 'get_rate_limit_stats' which serves a completely different purpose.

  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 provides some usage context: '可用于快速查找和预览' (can be used for quick lookup and preview), which implies this is for retrieval rather than analysis or processing. However, it doesn't explicitly state when to use this tool versus 'knowledge_search' (the sibling tool), nor does it mention any prerequisites or exclusions. The guidance is implied rather than explicit.

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

• get_rate_limit_statsB
  3.2/5.0
  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 current API rate limit usage statistics, implying a read-only operation, but doesn't specify whether it requires authentication, how frequently it can be called, what format the statistics are in, or any potential side effects. This leaves significant gaps in understanding the tool's 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 in Chinese that directly states the tool's purpose without any unnecessary words or fluff. It is front-loaded and appropriately sized for a simple tool with no parameters.

  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 simplicity (0 parameters, no output schema, no annotations), the description is complete enough to convey the basic purpose. However, it lacks details on output format, authentication needs, or rate limits, which could be helpful for an AI agent. Without an output schema, the description doesn't explain what is returned, leaving a gap in completeness.

  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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it could have mentioned that no inputs are required. Given the baseline for 0 parameters is 4, this score reflects that the description adequately handles the lack of parameters without redundancy.

  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 '获取' (get/retrieve) and the resource 'API速率限制使用情况统计' (API rate limit usage statistics), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like knowledge_recall or knowledge_search, which are unrelated to rate limits, so it misses the highest 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 or in what context it should be invoked. It lacks any mention of prerequisites, timing, or comparisons to other tools, leaving usage entirely implicit.

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

• knowledge_searchB
  3.2/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'AI-enhanced search' and 'deeply processed answers', but doesn't clarify what 'deep processing' entails, whether there are rate limits, authentication requirements, or what happens when multiple topic_ids are provided despite the note about current single support. The description adds some context but leaves significant behavioral aspects unspecified.

  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 brief (two short sentences) and front-loaded with the core functionality. Every sentence contributes value, though it could be slightly more structured. There's no wasted verbiage or redundancy.

  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?

  For a 5-parameter search tool with no annotations and no output schema, the description is moderately complete. It covers the core purpose and mentions conversation history support, but doesn't explain the nature of 'AI enhancement', what 'deep processing' means, or what the output format looks like. Given the complexity and lack of structured metadata, more behavioral context would be 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 already documents all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how parameters interact or provide usage examples. 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 tool performs 'AI-enhanced search' in the 'Get笔记知识库' and returns 'deeply processed answers'. It specifies the resource (knowledge base) and action (search with AI enhancement). However, it doesn't explicitly differentiate from sibling tools like 'knowledge_recall', which might have overlapping functionality.

  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 mentions 'support for conversation history follow-up questions', which implies usage in conversational contexts. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'knowledge_recall', nor does it mention any prerequisites or exclusions for usage.

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