AI 合规改写

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

With no annotations, the description carries the full burden. It discloses the rewrite action, the dependency on a prior detection, and the credit deduction side effect. It does not cover reversibility or detailed return format, but the key behavioral traits are exposed.

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 two short sentences, front-loaded with the main action, and every sentence adds value: purpose, prerequisite, and consequence. No filler or redundancy.

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?

The description explains the required workflow (must have check id, costs credits) and implies the return of a compliant version, which is sufficient given no output schema. It is complete for the main use case, though it omits edge cases like invalid checkId handling.

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 100%, so the baseline is 3. The description's mention of using check_text adds no new information beyond the schema's checkId description. Mode, strategy, and waitSeconds are fully documented in the schema.

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 triggers an AI rewrite for a completed text detection, producing a compliant version. It distinguishes itself from sibling tools by explicitly requiring a prior check_id from check_text, making its specific role obvious.

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 explicitly states the prerequisite of using check_text first, providing clear context for when to use the tool. However, it does not mention alternative tools or when-not-to-use conditions beyond the implicit credit cost, so it falls short of a 5.

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