Server Quality Checklist

Tool Scores

• search_wechat_articlesA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries the transparency burden. It explicitly discloses the data is an in-house snapshot (not real-time) with typical 1-2 day lag, and lists the returned fields, giving agents a clear expectation of behavior and output 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?

  Three sentences with clean line breaks, front-loading the core purpose and unique value before supporting details. No filler 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?

  The rich 5-parameter schema and presence of an output schema mean the description doesn't need to restate parameters or return structure. It adds the essential use case, ranking lists, and data freshness caveat. It could have explicitly named sibling tools to strengthen routing, but the context is sufficient.

  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 baseline is 3. The description itself adds no parameter-specific meaning; the schema already documents defaults, ranges, and caveats like '本数据源该参数真实生效' for days.

  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 opens with a clear verb+resource pair: '查公众号爆款文章' (search WeChat viral articles). It also claims to be the only data source with full text, distinguishing it from sibling tools like search_xiaohongshu_notes.

  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?

  It states the tool is best for topic benchmarking and writing style analysis, and notes the data is a non-real-time snapshot with 1-2 day lag, implying not to use it for real-time needs. It doesn't explicitly name alternatives, so it stops short of a 5.

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

• search_hot_topicsA
  4.5/5.0
  Behavior4/5

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

  With no annotations, the description carries the safety/behavior burden and does a good job: it discloses real-time freshness, topic-level granularity, absence of interaction counts, and the lack of Xiaohongshu data. It does not mention rate limits or auth, but this is a read-oriented search tool and the stated caveats are the most decision-relevant behaviors.

  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 compact, front-loads the scope, and uses formatting/bold to highlight the key distinction. Every sentence earns its place: coverage, data granularity, usage routing, freshness, and an exclusion caveat.

  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 an output schema exists and all four parameters are optional and documented in the schema, the description completes the picture: what the tool returns, when to use it versus alternatives, freshness, and an important data-source exclusion. No critical calling information is missing.

  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 coverage is 100%, so the schema already documents limit, keyword, platform, and max_boards fully. The description adds useful context ('85 boards', no Xiaohongshu, topic-level data) but does not materially change parameter semantics beyond the schema descriptions.

  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 opens with a specific verb and resource: '查全网实时热榜（85 个榜单）' and enumerates the covered platforms. It also differentiates itself from the content-search siblings by stating this is topic-level data (titles, ranks, heat values only) and that answering 'which content exploded' requires the other tools.

  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?

  Explicitly prescribes when to use this tool: answer 'what is trending now'; for content-level explosion questions it points to the two sibling search tools. It also gives a negative boundary ('本数据源不含小红书榜') and points to list_hot_boards in the schema for valid platform names.

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

• search_xiaohongshu_notesA
  4.5/5.0
  Behavior5/5

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

  With no annotations, the description carries full disclosure burden and does so excellently. It explicitly states known limitations: no note body, desc field often empty (44%) and why, and links lacking xsec_token causing 404s, plus the caveat that days is effectively non-functional. This is far beyond typical transparency.

  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 compact, front-loaded with the core purpose, then structured into return fields and known limitations. Every sentence adds value, and the known-limitations section is clearly separated for agent consumption.

  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 output schema exists and the description covers purpose, return fields, ranking types, parameter caveats, and known data-quality issues, it is complete enough for an agent to invoke the tool correctly and set user expectations appropriately.

  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 coverage is 100%, so the schema already documents days, limit, and keyword including the days ineffectiveness and cold-keyword caveats. The description adds context about returned data and link limitations, but it does not materially enrich parameter semantics beyond what the schema provides.

  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?

  Description opens with '查小红书爆款笔记', a specific verb, resource, and platform, and further specifies ranking categories and returned fields. This clearly distinguishes the tool from siblings like search_wechat_articles and list_hot_boards by platform and content type.

  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?

  Provides clear operational context: which rankings are available, what data is returned, and actionable keyword advice (cold words may return nothing, prefer popular track words). It does not explicitly contrast against sibling tools, but the platform and content-type focus make the appropriate usage obvious.

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

• list_hot_boardsA
  4.7/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries the behavioral disclosure burden. It discloses that the tool enumerates all available boards and that the result is meant to seed search_hot_topics' platform parameter. It does not explicitly state side-effect or rate-limit behavior, but an empty-parameter listing tool is clearly read-oriented.

  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?

  Two short sentences with no filler; the core purpose and output shape come first, followed by when-to-use guidance. Every sentence earns its place.

  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?

  For a zero-parameter listing tool with an output schema, the description fully covers what the tool returns, how it relates to search_hot_topics, and when to invoke it. Nothing essential is missing.

  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 zero parameters, so there is no schema burden and the baseline is 4. The description adds useful cross-tool context by linking the returned boards to search_hot_topics' platform parameter, though it does not need to explain any local parameters.

  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?

  States a specific action (列出) on a concrete resource (全网热榜里所有可用的榜单) and specifies the output fields: platform name, board name, and entry count. It also positions the tool as the source for search_hot_topics' platform parameter, making it easy to distinguish from the searching siblings.

  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?

  Gives explicit triggers: call it when the user asks which sites/platforms are queryable or when coverage is uncertain. This directly tells the agent when to use list_hot_boards instead of guessing or calling search_hot_topics.

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