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cn_data_v3_administrative_penalty

查询行政处罚决定、公示日期及处罚内容。 No independent entity identity, freshness, completeness, ownership, compliance or safety certification. Provider first page only, at most 5 rows, no pagination input. Empty is not a safety finding. Survey validated only an empty page; nonempty row semantics not yet independently verified. Use company_id from cn_data_v3_company_detail. 0.011000 USDC per single call. Pass arguments matching get_cn_data_tool_schema("v3_administrative_penalty"). One selected Chinese company data tool call. Additional pages require separate requests. No completeness or safety guarantee.

Input Schema

NameRequiredDescriptionDefault
argumentsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it discloses first-page-only retrieval, a 5-row cap, no pagination input, a per-call price of 0.011000 USDC, and that nonempty row semantics were not independently verified. It omits auth/permission requirements, but the limitation disclosures are unusually concrete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded in the first Chinese sentence, but the body is a repetitive hedge: 'No independent entity identity, freshness, completeness...' is restated by 'No completeness or safety guarantee', and 'first page only, at most 5 rows, no pagination input' is restated by 'Additional pages require separate requests.' Several sentences do not earn their place.

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?

The description covers the safety/limitation profile and data source, which is the critical context for a no-annotation, no-output-schema tool. It still leaves the shape of returned rows and the full argument contract dependent on an external schema call, so an agent lacks enough to call it confidently in one step.

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 0% and the single 'arguments' property is an opaque nested object with additionalProperties true. The description partially compensates by naming the company_id field and pointing to get_cn_data_tool_schema for the full argument contract, but it does not enumerate required fields itself.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening states a specific verb and resource: query administrative penalty decisions, publication dates and penalty content. It is clearly a distinct data resource, though it does not explicitly distinguish itself from similarly-scoped siblings such as cn_data_v3_tax_punishment_list.

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?

It gives operational prerequisites ('Use company_id from cn_data_v3_company_detail', 'Pass arguments matching get_cn_data_tool_schema(...)') and warns that an empty result is not a safety finding. However, it never states when to choose this tool over a related penalty or risk sibling, so alternative selection 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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