Extract all Chinese law article citations from a text and verify each against a local database. Returns invalid or fabricated citations to help detect model-generated false references.
抽取一段文本中的所有《法律》第 N 条引用并逐条核验，返回不存在的引用清单。
把模型生成的答案整段传进来，即可发现其中编造的引用。
Args: text: 待检查的文本（例如模型输出的一段法律分析）。
Returns: total / valid / invalid 计数，以及逐条核验结果。
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the extraction pattern, per-citation verification, and return counts, but omits whether the operation is read-only, requires network access, has rate limits, or has other side effects. For a verification tool, this is adequate but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and uses clear Args/Returns structure. It is slightly verbose but every section contributes useful information for calling the tool correctly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers purpose, usage, input semantics, and a summary of return values. It could mention edge cases (e.g., no citations found) or output shape more precisely, but it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 explains that the single parameter is the text to check and gives a helpful example (model output legal analysis), which adds meaning beyond the bare schema type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: extract all 《法律》第 N 条 citations from a text, verify them one by one, and return the list of non-existent citations. It clearly distinguishes itself from siblings like search_statutes and list_laws, though it does not explicitly name the alternative verify_citation for single-citation checking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives clear context by instructing to pass an entire model-generated answer to discover fabricated citations. This tells the agent when the tool is useful, but it does not mention exclusions or explicitly compare against sibling tools like verify_citation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.