chuance-policy-mcp

Server Quality Checklist

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Latest release: v0.1.3
Disambiguation4/5
Each tool has a distinct primary role: search, fetch full text, verify a single law article, verify citations in batch, check freshness, and list data sources. There is slight functional overlap (fetch_page_text and check_freshness both return publish dates, and verify tools fetch pages), but the descriptions clarify when each should be used.
Naming Consistency4/5
Most tools follow a readable verb_noun pattern: search_gov_policy, fetch_page_text, batch_verify_citations, check_freshness. However, verify_law_article_tool adds an unnecessary '_tool' suffix and source_catalog is noun-only, so the pattern is not perfectly uniform.
Tool Count5/5
Six tools is well-scoped for a specialized policy/law research MCP. Each tool serves a distinct step in the research workflow—search, fetch, verify, batch verify, freshness check, and source transparency—without redundancy or bloat.
Completeness5/5
The toolset covers the full core workflow: searching authoritative policy sources, retrieving page text, verifying individual and batch law citations, and assessing freshness. source_catalog also clarifies boundaries and data-source limitations, leaving no obvious dead ends for the stated purpose.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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
This repository is licensed under MIT License.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
无 annotations，描述承担行为披露责任。“返回”表明该操作不会产生副作用，并列出返回内容的三类信息，但未说明是否需要鉴权、调用频率限制或输出规模。对于无参数的只读信息工具而言，这种程度的披露基本合格但不够深入。
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?
描述只有一句话，核心信息全部前置，无冗余词。足以支撑一次正确的工具选择与调用。
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?
对于零参数、无输出 schema 且无嵌套对象的简单元信息工具，描述覆盖了用途和返回内容大类。唯一不足是没有明确说明调用顺序或“先调用本工具再选择兄弟工具”的强制关系，但“强制使用声明”已隐含该含义。
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?
输入 schema 无参数且 schema 描述覆盖率为 100%，0 参数场景下描述无需补偿。描述提到的“数据源清单、能力边界”等正是该工具返回的内容，与无参调用方式一致。
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?
明确使用动词“返回”并指出具体对象：“本 MCP 的数据源清单、能力边界与强制使用声明”，将其与 search_gov_policy、fetch_page_text 等面向具体内容的兄弟工具清楚区分。即便不查看 schema，也能判断这是一个关于 MCP 自身的元信息工具。
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?
描述通过“能力边界”和“强制使用声明”间接暗示应在选择其他工具前查阅本工具，但没有像“当…时使用本工具，否则使用…”这样明确给出与兄弟工具的决策规则。使用方法属于可推断而非明示。
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the behavioral burden. It discloses best-effort date parsing, includes text_truncated and retrieved_at in the return shape, and implies truncation behavior. It does not address failures or access restrictions, but that is a minor gap for a read-only fetch tool.
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 and front-loaded, with the core action stated first. The usage list and return shape are both relevant and contain no fluff.
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 description covers purpose, use cases, and the return contract explicitly, which is valuable since no output schema exists. However, it lacks detail about the max_chars parameter and does not explicitly guide when not to use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It never explains the url or max_chars parameters; url is inferable from context, but max_chars semantics and its relationship to text_truncated remain undocumented. This is a clear gap.
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 states a specific action and resource: fetching policy/regulation pages and extracting main text plus publish date. It also lists concrete use cases, making it clearly distinguishable from sibling search, verification, and catalog tools.
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 gives explicit usage contexts: reading full content of retrieved pages, checking clause context, and extracting issuing organ/date. It does not explicitly name exclusions or alternatives, but the context provided is clear enough for an agent to select it appropriately.
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 full burden. It does disclose the core behavior: fetching authoritative full text and returning the original article text plus source. However, it does not describe failure behavior when the law or article cannot be found, matching nuances for article number formats, or any network/authority dependencies.
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 and well structured: a one-sentence purpose, followed by clear Args and Returns sections. Every portion adds useful information, and the purpose is front-loaded.
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?
With two required parameters and no output schema, the description provides a Returns field list that gives the agent a good sense of the result shape. It is mostly complete, though it could briefly explain the meaning of fields like matched or disclaimer, or describe what happens on no-match, for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description fully compensates by explaining both parameters in detail: law_title expects the full law name with an example and notes that 书名号是可省的; article_no supports Chinese or numeric forms with concrete examples. This is exactly the kind of semantic detail an agent needs.
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 states a specific verb, '核验', and a specific resource, 《某法》第X条, while making clear the deliverable is the complete article text plus source. This clearly distinguishes it from sibling tools like search_gov_policy, fetch_page_text, and batch_verify_citations, which serve different purposes.
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 the tool is for verifying an exact law article by fetching authoritative full text, but it does not explicitly state when to use this tool versus the siblings. It lacks guidance on exclusions such as 'use search_gov_policy for policy search' or 'use batch_verify_citations for multiple citations at once.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does meaningful work: it lists the returned values (publish_date, days_old, timeliness_note), states that it measures days since publication, and explicitly disclaims official validity judgment. It does not mention error behavior or URL accessibility, but for a read-only freshness check this is reasonable.
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 sentences with no filler: the first states purpose and return fields, the second adds the necessary caveat about official validity. The most important limitation is positioned at the end without bloating the description.
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?
For a one-parameter tool with no output schema, the description supplies the key context: what it checks, what it returns, and what it does not decide. It could be more complete by describing the timeliness_note format and when to prefer an official validity tool, but it is sufficient for correct invocation.
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 description coverage is 0%, so the description must compensate. It clarifies that the single url parameter is the policy page whose publication date and age will be checked, and connects it to the tool's return values. It doesn't spell out URL requirements, but the semantic is effectively clear.
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 names a specific verb ('检查') and resource ('政策页面的发布日期与时效性'), and states the concrete output fields. It also differentiates the tool's limited role from an official validity determination, which sets it apart from siblings like verify_law_article_tool.
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 clearly frames the tool as an auxiliary date reference for questions about whether a document is still current, and warns that official timeliness markings govern invalidity. It does not explicitly name a sibling as the alternative for official validity determinations, so it stops short of full when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It explains the processing behavior (auto-parsing law names/article numbers, item-by-item verification), the concurrency constraint (serial to avoid blocking), and the output (a verification table with verified/pending markers). This meaningfully exceeds a generic 'verify citations' statement.
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 and front-loaded with the core purpose, followed by a concrete example, constraints, processing behavior, and output usage. Every sentence carries useful information and there is no redundancy or filler.
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 moderate complexity (single array parameter, no output schema, no annotations), the description is largely complete: input format, constraints, behavior, and return purpose are all covered. It could be more explicit about error handling or invalid citations, but what is provided is sufficient for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage for parameters is 0%, so the description must compensate. It fully does: it provides an exact example of the expected array format, clarifies that each string is a law citation, and specifies the maximum batch size of 8 items. This gives the agent everything needed to construct the parameter correctly.
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 states a specific verb+resource: '批量核验引用' (batch-verify citations), and provides a concrete example. It distinguishes itself from siblings such as verify_law_article_tool by emphasizing batch processing and parsing each citation, making the tool's exact role clear.
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 gives clear context for when to use the tool: when a list of citations needs batch verification, with a maximum of 8 items. It also states that it serves the citation verification skill's labeling workflow. It does not explicitly exclude single-article verification or 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it is transparent: it discloses that 'all' includes a Bing fallback, that sources are official interfaces including document number and validity metadata, and it specifies the exact return shape. No behavioral traits are hidden.
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 front-loaded with a one-sentence summary, followed by a compact Args block and a Returns block. Every line adds relevant information—examples, source meanings, and output fields—so nothing is wasted.
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 three-parameter tool with no output schema and no annotations, the description is complete. It defines all parameters, the available scopes, and the exact result structure, so an agent can call it accurately without additional information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, and it does thoroughly. Each parameter is explained: keyword with concrete examples, scope with the full enumeration of options and their meanings, and limit with an explicit upper bound (<=10).
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 states a clear verb-resource pair: '检索政策/法规/案例' (search policies/regulations/cases) across '权威渠道' (authoritative channels). The detailed scope list and the return fields make it unambiguous what the tool does, and the sibling tools are all different operations (fetch, verify, batch-verify, freshness, catalog), so it is clearly the search tool among them.
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 provides rich context on when to use the tool by enumerating the scope values and their meanings, such as 'state_council' for the Chinese government policy database and 'all' for a broader search with a Bing fallback. However, it does not explicitly state exclusions or direct alternatives (e.g., when to prefer verify_law_article_tool), so a point is withheld.
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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