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UK Data: US vehicle safety (NHTSA)

vehicle_models_for_make_year

Read-onlyIdempotent

Which models did this make produce in a given model year? Lists the model names NHTSA holds for a make and year, A to Z, so an agent can check the exact model name before a recall, complaint or crash-rating lookup. For dealers, form-filling and data cleaning. Source: NHTSA vPIC. It includes every vehicle type the make registered (cars, motorcycles, trucks) with US model names, and does not give trims or specifications. (cost: 1 credit)

Input Schema

NameRequiredDescriptionDefault
makeNostring, required, vehicle make, e.g. honda
limitNointeger, optional, max models to return, 1-100, default 25
modelYearNointeger, required, model year, e.g. 2019

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, open-world, so the safety profile is covered. The description adds real context beyond that: the data source (NHTSA vPIC), result ordering (A to Z), that all vehicle types (cars, motorcycles, trucks) are included, and a cost of 1 credit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core question, then scope, then audience, then caveats and cost. Information density is high, though the 'For dealers, form-filling and data cleaning' clause is the weakest sentence and could be trimmed without loss.

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?

With no output schema, the description carries the burden of describing returns and does so: a A-to-Z list of model names covering all vehicle types, explicitly excluding trims and specifications. Combined with source attribution and credit cost, an agent has everything needed to call it correctly.

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?

Schema description coverage is 100%, so make, modelYear, and limit are already documented, and the baseline is 3. The description reinforces that results are keyed to a make and a model year but adds no format, matching, or pagination detail for the limit parameter.

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?

States a specific verb and resource ('Lists the model names NHTSA holds for a make and year') and frames it as a question an agent would actually ask. It also delimits scope ('does not give trims or specifications'), which separates it from spec-oriented siblings like vehicle_driver_assist_features or vin_decode.

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?

Gives a concrete when-to-use: resolve the exact model name before a recall, complaint, or crash-rating lookup, which implicitly routes to vehicle_recalls, vehicle_complaints, and vehicle_crash_ratings. It also names audiences (dealers, form-filling, data cleaning) and an exclusion (no trims/specs), but never explicitly contrasts with wmi_manufacturer_lookup or vehicle_manufacturer_details.

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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