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

With no annotations, the description carries the full burden. It implies a read-only analysis and hints at detecting "honeypot-tell leakage," but does not say whether it is pure inspection, what findings or verdicts it returns, whether the sample is stored or retained, or any limits on payload size. The undefined term "honeypot-tell" further obscures actual behavior.

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?

A single dense sentence that front-loads the verb and the object of the audit; nothing is padding. It is slightly overloaded with an enumerated endpoint list, but the structure is efficient and readable.

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?

There is no output schema and no annotations, so the description should explain the return shape (verdict, violations, field-level mismatches) and clarify the novelty concepts it introduces. Without that, an agent can invoke it but cannot predict or interpret results, and cannot confidently distinguish it from the adjacent audit_* tools.

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 both endpoint and sample_payload documented including examples and a Zero-PII note, so the schema does the heavy lifting. The description's endpoint enumeration roughly duplicates the schema's own examples and adds no format, validation, or matching rules beyond it; baseline 3 is appropriate.

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 uses a specific verb ("Audit") on a defined resource (a sample JSON response) and enumerates the endpoint families covered (rerank, vLLM score, embeddings, Replicate prediction, image generation). This lets an agent recognize the tool's domain, though it never states what a successful audit produces, which leaves the boundary with sibling audit tools somewhat blurred.

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?

Usage is implied by the list of accepted payload types, so an agent can infer it should call this when it has a sample response from one of those endpoints. There is no explicit when-to-use versus alternatives guidance and no mention of when not to use it (e.g., request payloads, prompt hygiene, which sibling tools like audit_multimodal_prompt_hygiene appear to cover).

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