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VIN decoder: make, model, year, engine and plant from a VIN

vin
Read-only

Decode a 17-character vehicle identification number (VIN) with the US NHTSA vPIC database: make, manufacturer, model, model year, trim, body class, vehicle type, doors, drive, fuel and electrification, engine cylinders, displacement and horsepower, transmission, weight class and assembly plant, plus the check-digit test. Best for North American vehicles. Use for listings, insurance and fleet records. A malformed VIN is a 400, uncharged. Example: vin=1HGCM82633A004352. Price $0.01/call via x402 USDC or a card key (/credits), first 25 free; failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input Schema

NameRequiredDescriptionDefault
vinYes17-character VIN (letters I, O and Q never appear)
yearNoOptional model year, which helps decode older or partial VINs

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Goes well past the annotations: malformed VIN returns 400 and is uncharged, failed calls are free, each successful call costs $0.01 (first 25 free), and repeats are not free despite being a read. That last point resolves what the idempotentHint=false flag leaves ambiguous, and the billing model with an auth path (x402 USDC or card key) is disclosed up front.

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-loads the decode scope and fields, then follows with caveats, example and pricing. Dense but every clause is functional; the pricing/charging sentence is slightly chatty and runs long.

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 return set explicitly, plus cost model, error behavior and the read-only guarantee. An agent has everything needed to decide and call correctly.

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?

Schema coverage is 100%, so both parameters are already documented, and the description reinforces the 17-character constraint with a concrete example VIN. The optional year parameter and its purpose for older/partial VINs are covered only by the schema, but the example adds real value over the baseline.

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+resource (decode a 17-character VIN) and enumerates exactly what comes back (make, model year, engine, plant, check digit). Nothing in the sibling list overlaps, and the NHTSA vPIC attribution tells the agent which data source it is querying.

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?

"Best for North American vehicles. Use for listings, insurance and fleet records" gives clear usage context and a scope limitation. It stops short of naming a when-not or an alternative for non-North-American VINs, so no explicit routing.

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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