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

The annotations convey only that this is a mutating, non-destructive action, so the description adds real value by explaining the re-call/update behavior, that prior feedback is retained, and that the returned state is the source of truth. It also explicitly directs agents to rely on the `summary` field. It could go slightly further on failure behavior or side effects, but the upside is good.

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 four sentences and every sentence earns its place, covering purpose, the conditional feedback constraint, the update behavior, and how to interpret the response. No filler, redundant examples, or redundant schema repetition.

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 7-parameter schema, 100% schema coverage, and an output schema, the description provides the missing behavioral context needed for correct invocation and response interpretation. It could be improved by explicitly stating that the document_id must belong to a conversion performed by the caller, but it is still adequately complete.

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 schema already documents every parameter. The description restates the conditional feedback rule already present in the schema and broadly mentions use-case tags, but it does not add much new meaning about parameters like export_format or use_case_other, so the baseline of 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 clearly states the verb and resource: report how well a previously converted bank statement was parsed, with a 1-5 rating, optional feedback, and use-case tags. It is unambiguous and reads distinctly from the sibling tools, though it does not explicitly name or differentiate itself from a sibling like evaluate_benchmark.

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 makes the usage context clear: rate an already converted statement, and re-calling updates the existing rating. It also warns that there is no separate read-back tool, which guides an agent toward using the response for that purpose. It does not explicitly list alternatives or when-not-to-use scenarios, but the tool's role is fairly self-contained.

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