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

Annotations already establish this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral detail beyond those flags: it names NHTSA as the source, says it returns all recall campaigns, and enumerates the returned fields (affected component, description, safety risk, recommended remedy).

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 front-loaded with the core purpose and returns format, then adds usage examples and scope. It is slightly repetitive in saying 'any vehicle recall check' after listing concrete examples, but every sentence contributes useful information for an agent.

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?

With no output schema, the description compensates well by stating the input dimensions, the NHTSA source, the returned recall fields, and the coverage scope. It does not mention edge cases such as what happens when no recalls are found or invalid combinations are passed, but overall it is complete for a simple read-only lookup.

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 each parameter already described and given an example in the schema. The description only restates year/make/model in a natural-language example and adds no new parameter-level semantics, so the baseline score of 3 applies.

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 opens with a specific action and resource: 'Check for safety recalls on a vehicle by year, make, and model.' It further distinguishes this tool from recall siblings by specifying NHTSA as the data source and limiting scope to US vehicles from all manufacturers.

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 gives explicit user-intent examples ('are there recalls on my car?', 'check recalls for 2020 Toyota Camry', 'any open recalls?') and says to use it for any vehicle recall check. It does not explicitly state when to prefer sibling tools like CPSC or FDA recall searches, but the NHTSA/US-vehicle scope implies those exclusions.

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