Vehicle recalls

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds genuine behavioral context beyond that: VIN input is decoded with its check digit verified, and the caveat that recall campaigns apply to the model, not proof a given VIN is unrepaired, sets clear expectations about result interpretation. 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?

Two sentences with no filler; the first front-loads the purpose, lookup modes, and output fields, and the second adds a single high-value caveat. Every clause 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?

Given an output schema covers return structure and annotations cover the read-only/idempotent profile, the description covers purpose, lookup modes, output contents, and a key interpretive caveat. The main gap is parameter combination semantics (all four params optional, alternative lookup modes), which an agent would need to invoke correctly.

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?

With 0% schema description coverage, the description carries the full burden of parameter meaning and largely meets it: it explains that vin is one lookup key (decoded, check-digit verified) and that make, model, and year form an alternative lookup path. It does not clarify combination rules, such as whether VIN and make/model/year are mutually exclusive or what happens if no parameters are supplied.

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 names a specific verb and resource — 'NHTSA safety recalls for a vehicle' — and enumerates the two lookup modes (VIN or make/model/year) plus the exact fields returned (campaign number, date, component, summary, consequence, remedy, park status). The closing caveat about model-level applicability clarifies what the tool does and does not prove, which helps an agent distinguish it from a repair-status checker.

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 implies when to use the tool — when recall information for a vehicle is needed — and warns that results are model-level, not proof a VIN is unrepaired. However, it never names the sibling tools (recent_recalls, check_recalls) or states when one should be preferred, so routing guidance is left implicit rather than explicit.

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