NHTSA Vehicle Safety

get_complaints

Get consumer complaints about vehicles filed with NHTSA.

Search for safety complaints by make, model, and/or year. Returns
complaint descriptions, components involved, and crash/injury data.
At least one filter (make, model, or year) should be provided.

Args:
    make: Vehicle manufacturer name (e.g. 'Toyota', 'Ford').
    model: Vehicle model name (e.g. 'Camry', 'F-150').
    year: Model year (e.g. 2023).
    limit: Maximum number of complaints to return (default 25).

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses return content (complaint descriptions, components, crash/injury data) and the default limit, but does not mention potential error behavior if filters are missing, pagination, or explicit read-only nature. It's adequate but not rich in behavioral context.

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 concise and well-structured, with an introductory sentence followed by a clear Args list. Every sentence adds value, and it avoids unnecessary verbosity.

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?

Given the presence of an output schema and moderate complexity, the description sufficiently covers search criteria, return content, and the limit. It lacks minor details like pagination, but overall it is complete for the tool's purpose.

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

Parameters4/5

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

The schema has no property descriptions, but the tool description explains each parameter with examples (e.g., 'Toyota', 'Camry') and the default limit. It also adds the critical constraint that at least one filter is required, going beyond the 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 'Get consumer complaints about vehicles filed with NHTSA,' using a specific verb and resource. It distinguishes itself from sibling tools by focusing on complaint descriptions, components, and crash/injury data rather than trends, VIN decoding, or recalls.

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?

It provides clear usage context—search by make, model, and/or year—and importantly states that at least one filter should be provided. However, it does not explicitly compare with alternatives or say when not to use it, so it falls short of the highest bar.

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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TDQS

A4.2/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose with no ambiguity. complaint_trends_by_component analyzes component-level trends, decode_vin decodes VINs, get_complaints retrieves complaint details, get_crash_statistics provides fatal crash data, get_recalls fetches recall information, and local_vehicle_safety_profile combines national trends with local crash data. The boundaries between tools are well-defined, preventing misselection.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern with minor deviations. Most tools use get_ or decode_ prefixes (get_complaints, get_recalls, decode_vin), but complaint_trends_by_component and local_vehicle_safety_profile deviate slightly by using descriptive phrases instead of simple verbs. However, all names are snake_case and clearly describe their functions, maintaining readability.

Tool Count5/5

With 6 tools, the count is well-scoped for the NHTSA vehicle safety domain. Each tool earns its place by covering distinct aspects: VIN decoding, complaint retrieval, recall searches, crash statistics, trend analysis, and safety profiling. This provides comprehensive coverage without being overwhelming or too sparse.

Completeness5/5

The tool surface provides complete coverage for the NHTSA vehicle safety domain. It includes data retrieval (complaints, recalls, crash stats), analysis (trends, safety profiles), and utility functions (VIN decoding). There are no obvious gaps—agents can perform typical workflows like assessing vehicle safety, investigating defects, or analyzing crash data without dead ends.

Resources

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