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

Annotations already declare readOnlyHint=false, destructiveHint=false, and idempotentHint=true, so no contradiction. The description adds behavioral context beyond annotations — this is the precision-flywheel label source and feedback sharpens future inspections for everyone — which helps an agent understand downstream effects.

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?

Two tightly written paragraphs with no filler. The first gives function and constraints; the second gives the when-to-use context. Every sentence earns its place.

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?

For a 3-parameter, 2-required feedback tool with an output schema and informative annotations, the description is close to complete. The only minor gaps are side effects like batching or timing of learning, but these are not required for a correct call.

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 0%, so the description must carry the semantic burden; it does. It tells the agent verdict must be 'up' or 'down', note is optional free text, and warrant_id refers to the warrant whose rating is being labeled. Without this description, the agent would have no way to know the only valid verdict values.

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 ('Record thumbs up/down') on a specific resource ('a warrant's rating') and ties it to the engine's precision-flywheel label source. This distinguishes it from siblings like verify_document, which performs verification rather than outcome feedback.

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?

It gives an explicit trigger condition ('when the ground truth became known after a verify_document call'), concrete examples ('later confirmed genuine or fraudulent'), and the reason to use it ('so the engine learns from the outcome'). The when-to-use guidance is unambiguous.

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