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

With no annotations, the description carries the full burden and fully discloses key behaviors: it returns a specific set of fields, orders newest first, caps at 50 results, and costs $0.005 USDC via x402. It also warns that a zero count does not prove a vehicle is recall-free, a critical nuance that prevents misinterpretation of negative results.

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 four sentences with no filler. It front-loads the purpose and output fields, then provides query modes, usage scenarios, a critical caveat, and cost information. Every sentence adds value; there is no repetition or unnecessary detail.

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?

Despite no annotations and no output schema, the description provides enough to call the tool correctly: query parameters, output fields, ordering, limit, cost, and a caveat. It doesn't detail error handling or what happens if both make and campaign are used, but these are minor gaps given the clear schema and the tool's simple read-only nature.

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?

The schema already provides detailed descriptions for all four parameters (100% coverage), so the baseline is 3. The description adds the conditional requirement that make is required unless campaign is provided, and clarifies the two query modes (by make/model/year or by campaign). This enriches the schema without redundancy, though it doesn't introduce entirely new semantics.

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: returning official NHTSA safety recalls for a vehicle, and lists the specific output fields (campaign id, affected component, safety consequence, remedy). It distinguishes itself from sibling tools (dtc_lookup, vehicle_tsbs, vin_decode) by focusing exclusively on recalls, so an agent can select it unambiguously.

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 use cases: before buying a used vehicle or when a customer asks about open recalls. It does not explicitly name alternatives, but the sibling names (dtc_lookup, vehicle_tsbs, vin_decode) make it clear they serve different purposes. The caveat about zero count also guides interpretation of results, helping agents understand when not to over-conclude.

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