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lzinga

US Government Open Data MCP

nhtsa_recalls

Read-only

Search vehicle recalls from NHTSA by make, model, and model year to find safety issues affecting your car.

Instructions

Search NHTSA vehicle recalls by make, model, and model year. All three parameters are required by the NHTSA API. Use nhtsa_models to find valid models for a make, or nhtsa_recall_detail for a specific campaign.

Example: make='tesla', model='model 3', model_year=2024

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
makeYesVehicle make: 'toyota', 'ford', 'tesla', 'honda'
modelYesVehicle model: 'camry', 'f-150', 'model 3', 'civic'
model_yearYesModel year: 2020, 2023, 2024
Behavior3/5

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

The annotations already declare readOnlyHint=true, so the description's 'Search' aligns with that. The description adds the constraint that the NHTSA API requires all parameters, which provides some behavioral context beyond annotations, but not extensive traits like 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 concise, consisting of three short sentences and an example. It front-loads the main purpose, then adds requirement and alternative tool guidance, and a concrete example. Every sentence is purposeful and no waste.

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 the tool's simplicity (3 required parameters, no output schema), the description covers the key aspects: purpose, required parameters, usage example, and alternatives. It is complete for an agent to understand how and when to invoke this 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% coverage with descriptions for all three parameters. The description adds an example with specific values (make='tesla', model='model 3', model_year=2024), which offers formatting guidance but does not significantly extend beyond the schema's existing descriptions.

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 action ('Search'), resource ('NHTSA vehicle recalls'), and required parameters (make, model, model year). It also mentions sibling tools nhtsa_models and nhtsa_recall_detail for alternative use, distinguishing this tool's purpose.

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

Usage Guidelines5/5

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

The description explicitly states that all three parameters are required by the NHTSA API, provides an example usage, and directs users to nhtsa_models for valid models or nhtsa_recall_detail for specific campaigns, offering clear when-to-use and alternative guidance.

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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