Every NHTSA recall campaign on file for one model year, with severity, park-outside and do-not-drive advisories and the reported completion rate. IMPORTANT: recalls apply to a build range, not to a model year, so this cannot establish whether one specific vehicle is affected — only a VIN check at nhtsa.gov/recalls can.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Vehicle id, e.g. 2021_kia_telluride. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does so well. It discloses what the tool returns (severity, advisories, completion rate) and highlights the critical behavioral limitation that model-year scope does not equal per-vehicle status. It could add minor details like empty-result behavior, but the essential traits are transparent.
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 two sentences with no filler. The first sentence front-loads the core purpose and return contents; the second adds the crucial caveat. Every word 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 no output schema and no annotations, the description supplies sufficient context: it enumerates the output fields and warns about the model-year limitation. It could elaborate on the exact response format, but for a one-parameter lookup this is a complete and usable definition.
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 100% coverage for the single id parameter with an example ('2021_kia_telluride'). The description implies that the id encodes a model year, but it adds no new parameter-level meaning beyond what the schema already states, so the baseline 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 states an exact verb-resource relationship: it returns every NHTSA recall campaign for a model year, with specific data fields. It also distinguishes itself from VIN-level lookups by explicitly stating it cannot determine whether a specific vehicle is affected, which separates it from sibling tools like decode_vin.
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 when-not-to-use guidance: because recalls apply to build ranges rather than model years, this tool cannot confirm a single vehicle's recall status. It names the alternative — a VIN check at nhtsa.gov/recalls — leaving no ambiguity about when a different approach is required.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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