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Vehicle Consumer Complaints

vehicle.safety.complaints
Read-onlyIdempotent

Search NHTSA consumer vehicle complaints for safety research and product liability analysis. Retrieve incident details including crash/fire flags, injuries, deaths, and affected components.

Instructions

Search consumer complaints filed with NHTSA about vehicles. Returns incident details including crash/fire flags, injuries, deaths, affected components, and complaint summary. Covers US vehicles from ~1995 to present. Critical for safety research and product liability analysis (NHTSA)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
makeYesVehicle manufacturer name (e.g. "Toyota", "Ford", "Tesla", "Honda")
modelYesVehicle model name (e.g. "Camry", "Model 3", "Civic")
model_yearYesModel year (e.g. 2023). NHTSA complaint data available from ~1995 to present

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
Behavior5/5

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable details about what the tool returns (crash/fire flags, injuries, deaths, components, summary) and its coverage (US vehicles from ~1995 to present), which goes beyond annotation information.

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 three sentences with no wasted words. The key action and resource are front-loaded in the first sentence. It efficiently conveys purpose, return data, and coverage.

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?

Given that an output schema exists (not shown but mentioned), the description sufficiently explains the return fields and data range. It covers the tool's purpose, input requirements, and output content, making it complete for an agent to select and invoke the tool.

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

Parameters3/5

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

The input schema has 100% description coverage, so the schema already explains each parameter (make, model, model_year). The description adds no additional parameter semantics beyond what the schema provides, so baseline score of 3 is appropriate.

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 it searches consumer complaints filed with NHTSA, specifying the resource (complaints) and action (search). It distinguishes itself from sibling tools like recalls and investigations by focusing on complaints.

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?

While the description mentions it is critical for safety research and product liability analysis, it does not explicitly guide when to use this tool versus alternatives like vehicle.safety.recalls or vehicle.safety.investigations. However, the context of complaints versus recalls or investigations is implicit.

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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