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savantcat

savantcat-ai-compliance-mcp

Official
by savantcat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_article

Read-onlyIdempotent

Retrieve the exact text of a specified article from Chinese AI regulations by law and article number, including content fingerprint and official source link for citation and verification.

Instructions

按条号取某部法规的条文逐字原文，并附内容指纹与官方原文链接。

与 get_requirement 的分工：get_requirement 按「问题」取整条合规要求（含结论与落地动作）；
get_article 按「条号」取条文原文，用于逐字核对、引用与溯源。

Args:
    law: 法规名或集群标识。可写全称（《生成式人工智能服务管理暂行办法》）、
         简称（标识办法 / 算法推荐规定）或 cluster
         （genai-interim / ai-content-label / deep-synthesis / algo-recommendation）
    article: 条号，支持「第十条」「10」「第10条」三种写法

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lawYes
articleYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.1

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds the meaningful guarantee that the returned text is verbatim原文 and includes a content fingerprint and official link for traceability, which is behavioral context beyond the structured fields.

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 purpose and the sibling differentiation are front-loaded, followed by a clean Args block. It is slightly verbose across the purpose and division-of-labor sentences, but every part earns its place and nothing is padded.

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?

With an output schema present, return values need not be explained, and the description still notes the key returned artifacts (原文, fingerprint, link). Inputs are fully documented despite 0% schema coverage, and the rich annotations cover safety, leaving no gap for correct 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 description coverage is 0%, so the description carries the full burden and does so thoroughly: law accepts full names, abbreviations, or cluster identifiers (with concrete examples), and article accepts three numbering formats (第十条 / 10 / 第10条). This is strong compensation well above the baseline.

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 verb (取/取条文原文) and resource (某部法规的条文), and immediately names what it returns: verbatim original text plus content fingerprint and official source link. It explicitly distinguishes itself from get_requirement, so an agent can tell the two apart without opening a schema.

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?

Provides an explicit division of labor: get_requirement by question (with conclusions and action items) vs get_article by article number, and names the scenarios (逐字核对、引用与溯源) that select this tool. Nothing about when to prefer it is left to inference.

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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