api-automation

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions generation of multiple artifacts but lacks details on side effects, authentication requirements, or whether files are created/modified. The transparency is limited to high-level functionality.

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 a single concise sentence that efficiently conveys the tool's core function. It is not verbose and gets to the point, though it could benefit from slightly more detail. No wasted words.

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?

Given the single parameter, no output schema, and no annotations, the description is minimally adequate. It clarifies the input and general output artifacts but lacks specifics about output format, error handling, or any prerequisites. For a tool of this apparent complexity, it is somewhat incomplete.

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 coverage is 100% with a description for the single parameter 'apiDocs' ('后端接口文档内容'). The tool description does not add additional parameter-specific semantics beyond what the schema provides, so it meets the baseline of 3.

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 the tool generates interface types, address constants, mock data, and request functions from backend API documentation. The verb 'understand and generate' combined with the target artifacts makes the purpose specific, and it is clearly distinct from sibling tools focused on Figma/SVG assets.

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 usage when one needs to process backend API documentation into structured artifacts, but it does not explicitly state when to use this tool versus alternatives or provide any usage constraints. No exclusions or context about when not to use it are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.