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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety behavior is covered. The description adds meaningful behavioral context by stating it returns 'all NHTSA recall campaigns' and enumerates the output fields (affected component, description, safety risk, recommended remedy), plus US vehicle/manufacturer coverage. 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 compact and front-loaded: the opening sentence states the core purpose, the second explains the return value, and the third gives concrete usage examples and coverage scope. The example list is slightly repetitive but useful for intent matching, and no sentence is wasted.

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 there is no output schema, the description compensates by specifying exactly what will be returned (recall campaigns with component, description, safety risk, remedy) and the coverage scope (all US vehicles). The required inputs are unambiguous. It could have mentioned when to use a VIN-based sibling, but the definition is complete enough for correct invocation.

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%, with each parameter already documented with clear examples. The description only restates the input combination in prose ('by year, make, and model') without adding new parameter-level semantics, so the baseline score 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 states a specific action ('Check for safety recalls') on a specific resource ('a vehicle by year, make, and model'), and clarifies it returns NHTSA recall campaigns. This clearly distinguishes it from product, drug, food, or device recall tools among the siblings, and the example queries further pin down the intended use.

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 provides clear invocation context by listing representative user phrasings like 'check recalls for 2020 Toyota Camry' and 'any open recalls?', and covers the general case with 'or any vehicle recall check'. However, it does not explicitly contrast with alternatives such as vin_decode for VIN-based queries or CPSP/FDA recall tools, so it lacks when-not guidance.

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