NHTSA Vehicle Safety

complaint_trends_by_component

Get complaint counts by vehicle component for a specific make/model/year.

Shows which parts of the vehicle consumers report the most problems with
(e.g. brakes, engine, electrical system). Useful for identifying systemic
vehicle defect patterns.

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

Input Schema

TableJSON Schema

Name	Required	Description	Default
`make`	Yes
`year`	Yes
`model`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4/5.0

Behavior2/5

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

With no annotations, the description has full burden for behavioral disclosure. It only states the output (counts by component) and a use case, but does not mention any side effects, data source limitations, or whether the operation is read-only. For a query tool this is somewhat implicit, but the lack of any behavioral context beyond results is a gap.

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 front-loaded with the main action, followed by a brief explanation, a use-case sentence, and a clean Args list. Every sentence earns its place, with no filler or repetition.

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, the description adequately covers the tool's purpose, parameters, and typical use case. It does not mention edge cases (e.g., no data for a given combination) but is otherwise complete for this simple query tool.

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 input schema has 0% description coverage, but the description's Args section fully compensates by defining each parameter (make, model, year) with clear examples ('Toyota', 'Camry', 2023). This adds meanings the schema does not provide.

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 opens with a specific verb+resource+scope: 'Get complaint counts by vehicle component for a specific make/model/year.' It clearly distinguishes the tool from siblings like get_complaints (raw complaints) and get_crash_statistics.

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 provides a clear use case ('Useful for identifying systemic vehicle defect patterns') but does not explicitly name alternatives or state when to avoid this tool. This is more specific than most, but falls short of explicit when/when-not guidance.

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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