self_check
按场景出合规自查清单:把命中条目的「落地动作」汇总成可勾选待办,并列出条文依据。
Args:
scope: 场景或关键词,如「AI 客服」「营销文案 标注」「小程序 上架」「全量」;
留空则返回全量自查清单
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
按场景出合规自查清单:把命中条目的「落地动作」汇总成可勾选待办,并列出条文依据。
Args:
scope: 场景或关键词,如「AI 客服」「营销文案 标注」「小程序 上架」「全量」;
留空则返回全量自查清单
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that matching entries are filtered and their '落地动作' are aggregated into a checklist, but it does not discuss error handling, rate limits, or other edge-case behavior. There is no contradiction with the annotations.
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 one focused purpose sentence followed by a compact parameter explanation with examples. It is front-loaded, contains no filler, and avoids repeating schema-only details.
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 single-optional-parameter tool with annotations and an output schema, the description covers purpose, scope semantics, and default behavior well. It does not position itself against sibling tools, but that is a minor gap for basic invocation.
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?
The input schema only provides the parameter name, type, and default, so the description carries the full semantic burden. The Args block defines scope as a scenario or keyword, gives concrete examples, and documents the empty-string default, fully compensating for the 0% schema description coverage.
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 clearly states a specific purpose: generate scenario-based compliance self-check lists by aggregating matching items into checkable todos and listing legal bases. It is specific about the output and gives example scopes, but it does not explicitly distinguish itself from sibling tools like search_compliance or get_requirement.
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?
The description implies when to use the tool through '按场景出合规自查清单' and provides example scope values, plus the blank-for-full behavior. However, it does not mention alternatives or state when not to use this tool, so routing between siblings 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.
Add one secure layer between your agents and this server.