NHTSA Vehicle Safety

get_recalls

Get vehicle safety recalls from NHTSA.

Search for recalls by make, model, and/or year. Returns recall campaigns
including the defect description, remedy, and affected vehicles.
At least one filter (make, model, or year) should be provided.

Args:
    make: Vehicle manufacturer name (e.g. 'Toyota', 'Ford', 'Honda').
    model: Vehicle model name (e.g. 'Camry', 'F-150', 'Civic').
    year: Model year (e.g. 2023).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`make`	No
`year`	No
`model`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedApr 3, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description carries the burden of disclosing behavior. It notes the return includes recall campaigns, defect description, remedy, and affected vehicles, and implies a read-only operation via 'Get'. It lacks explicit mention of safety, rate limits, or error handling, but these are less critical for a simple data retrieval tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured: a purpose statement, search/return details, a requirement note, and an Args list. Every section serves a purpose and no content is wasted, though the Args list adds length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is adequately described for its simplicity, with return content and input requirement specified. The presence of an output schema covers return structure. However, it does not explain error behavior when no filter is provided, nor does it reference sibling tools for comparison, leaving minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has zero parameter descriptions, so the description's Args section fully compensates by defining make, model, and year with examples. This provides clear semantics beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves vehicle safety recalls from NHTSA, with search by make/model/year. This distinguishes it from sibling tools like get_complaints and get_crash_statistics, which address different NHTSA datasets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the search capability and specifies that at least one filter should be provided, giving clear context for invocation. However, it does not explicitly mention when to choose this over sibling tools or when not to use it.

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

All NHTSA Vehicle Safety tools Inspect

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