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

Annotations already flag readOnlyHint=false, idempotentHint=false, and destructiveHint=false. The description adds that feedback is used for model recalibration and includes return keys, which is useful, but it does not clarify what `accepted: false` means or discuss side effects. There is no annotation contradiction.

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 compact, well-structured into Args and Returns, and leads with a clear purpose sentence. The Args section duplicates the schema, but it is brief enough not to feel bloated.

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 simple parameter set and high schema coverage, the description is largely complete. It includes return value shape, which is important because there is no output schema. It could be slightly more complete by explaining the `accepted` boolean, but this is a minor gap.

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 event_id, label, and notes. The description's Args section restates these without adding new meaning, achieving the baseline for high schema 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 states a specific action ('Report the confirmed outcome of an event') and a clear resource (the event outcome), with the purpose of recalibrating the fraud model. This distinguishes it from the sibling fraud_score tool, which presumably produces initial scores rather than consuming confirmed outcomes.

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 phrase 'confirmed outcome' clearly implies this tool is for post-event feedback rather than initial scoring, and the purpose of recalibration gives the agent context on when it is appropriate. However, it does not explicitly name alternatives or state when not to use this tool, so the guidance relies on inference.

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