NHTSA Vehicle Safety

local_vehicle_safety_profile

Get a vehicle safety profile using national complaint and recall trends.

NHTSA complaints are not geocoded by state, so this returns national-level
trends as context for local community safety assessments. Includes the most
recent recalls and top complained-about vehicle makes.

Args:
    state: Two-letter state abbreviation (e.g. 'CA', 'TX'). Used for crash
        statistics; complaint data is national.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`state`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the key behavioral caveat that NHTSA complaints are not geocoded by state, and clarifies that state is used only for crash statistics. It also describes the returned content (recalls, top makes). This goes well beyond the schema, though it does not mention rate limits or error handling.

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 well-structured and appropriately sized: a clear opening sentence, a concise caveat, and a focused Args section. Every sentence adds value, and the most important information is front-loaded.

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

Completeness5/5

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

The tool has a simple input (one state parameter) and an output schema (not shown but present). The description covers what the tool returns, the data sources, and the key limitation. It provides sufficient context for an agent to select and invoke the tool effectively, especially given the sibling tools that focus on narrower data.

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?

Although the schema has 0% description coverage, the description provides detailed parameter semantics in the Args section: format ('Two-letter state abbreviation'), examples ('CA', 'TX'), and purpose ('Used for crash statistics; complaint data is national'). This fully explains the parameter beyond the bare schema definition.

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?

Description uses a specific verb ('Get') and resource ('vehicle safety profile'), and clearly states the content: national complaint and recall trends, most recent recalls, top complained-about makes. It also distinguishes itself from siblings by explicitly noting that complaints are national, not state-level, which differentiates it from get_crash_statistics and get_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?

The description provides clear usage context: 'as context for local community safety assessments' and explains the data limitation (complaints not geocoded). It implies when to use this tool (when needing a national composite profile) but does not explicitly list alternatives or exclusions. This is strong but not fully explicit.

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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