Label a recall decision

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

Annotations provide `destructiveHint: false` and `readOnlyHint: false`, but the description adds unique context: labeling is write-once (idempotent behavior), free (never billed), and unlocks per-tenant threshold personalization. No contradiction with annotations.

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 concise (four sentences) and front-loaded with the core purpose. Each sentence adds essential information: purpose, usage constraints, write-once policy, and benefits. No redundant text.

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 tool has only two parameters and an output schema, the description fully covers all critical aspects: when to call, not to self-grade, idempotency, cost, and personalization outcome. No gaps remain for an agent to misuse the tool.

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 descriptions for both parameters. The description reinforces the meaning: `event_id` must come from a recall call and `correct` is based on user feedback. It adds no new technical details beyond the schema, so 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 clearly states the tool's purpose: 'Label a gated/verified recall decision as right or wrong.' It specifies the verb 'label' and the resource 'recall decision', distinguishing it from sibling tools like 'recall_gated' and 'memory_recall' by focusing on the labeling action after user confirmation.

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?

Provides explicit instructions: 'Call this ONLY when the USER has explicitly confirmed or corrected a recalled answer... NEVER label from the model's own judgment... Labels are write-once: an already-labeled result means do not retry.' This clearly defines when to use and when not to, and includes forward-looking guidance.

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