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guangxiangdebizi

China-Central-Policy-MCP

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_latest_policies searches and filters policies, while get_policy_fulltext retrieves the full text and normalizes it. There is no overlap.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_latest_policies, get_policy_fulltext), using 'get' as the verb and descriptive nouns.

    Tool Count4/5

    With only 2 tools, the server is minimal but well-scoped for a focused domain (search and retrieve policy full texts). It could benefit from additional tools like browsing by category, but the current count is reasonable.

    Completeness4/5

    The core workflow of searching for policies and retrieving their full text is covered. Minor gaps exist, such as the absence of a tool to list policies without keyword search or direct ID lookup, but overall it is sufficiently complete for its stated purpose.

  • Average 3.8/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    No annotations are present, and the description only mentions the API source and features. It fails to disclose behaviors such as rate limits, pagination, sorting, or response format, leaving significant gaps for an AI 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, front-loaded sentence with 17 words, efficiently conveying the core purpose without 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 search tool with no output schema, the description lacks details on return format, pagination, or effective usage. However, the schema fully documents parameters, and the sibling tool provides some context.

    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 description adds minimal value beyond the schema. It reiterates 'keyword search and date filtering' which aligns with the schema, but does not provide additional contextual meaning.

    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 searches State Council policies via gov.cn API with keyword and date filtering, distinguishing it from the sibling tool 'get_policy_fulltext'.

    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 this tool is for searching policies, while 'get_policy_fulltext' is for retrieving full texts, but it does not explicitly state when to use this tool or provide alternatives.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the burden. It states that the tool fetches and normalizes content, indicating a read operation. However, it does not disclose potential failure modes, permissions needed, or rate limits. The optimization note adds some context but not full 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 a single sentence that immediately conveys the action and result. It includes essential detail (the output fields) and a relevant optimization note. No redundant words; it is efficiently front-loaded.

    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 complexity of fetching and parsing web pages, the description provides moderate completeness. It explains the output format but omits error handling, timeouts, or limitations (e.g., only works on gov.cn). With no output schema, more detail on potential failures would improve 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?

    Schema coverage is 100%, so the schema already describes parameters. The description adds value by specifying the output structure (title, date, doc_no, body, issuer), which is not in the schema. This helps the agent understand what the tool returns, beyond what the input 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?

    The description clearly states what the tool does: fetch policy full text and normalize into structured JSON with specific fields (title, date, doc_no, body, issuer). It also specifies the optimization for gov.cn pages, which distinguishes it from the sibling tool get_latest_policies that lists policies rather than fetching full text.

    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 does not provide explicit guidance on when to use this tool versus alternatives. It implies that it is for fetching full text of a specific policy, and the sibling is for listing, but it lacks explicit when-not or alternative usage notes.

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

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