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Car Check

Decode a VIN

decode_vin
Read-onlyIdempotent

Use this when the user gives a 17-character vehicle identification number (VIN) and wants to know what car it is, such as "what car is 1HGCM82633A004353?". Returns the model year, make, model, trim, body class, engine, fuel, plant country and NHTSA's error codes and error text, which flag a wrong check digit or an incomplete decode. US-market vehicles. A data lookup, not a history report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYesThe 17-character vehicle identification number (letters and digits, no I, O or Q)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYes
fuelNo
makeNo
trimNo
modelNo
engineNo
noticeYes
statusYes
messageNo
body_classNo
error_textNo
model_yearNo
error_codesYes
plant_countryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world). The description adds traits the annotations cannot express: a US-market coverage limitation and the presence of NHTSA error codes/error text that flag a wrong check digit or incomplete decode — a real caveat an agent needs to interpret results.

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?

Front-loads the trigger condition, then the example, then the return payload, then the scope caveat. Every sentence carries distinct information with no filler.

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?

For a single-parameter read-only lookup, the description covers trigger, scope limits (US-market), return fields, and error semantics. Even though an output schema exists, the error-code explanation adds interpretive value; nothing needed to invoke it correctly is missing.

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?

Only one parameter and schema coverage is 100%, so the schema already documents the 17-character format and the I/O/Q exclusion. The description's mention of '17-character vehicle identification number' merely restates the schema, adding no syntax or format value beyond it.

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 VIN) and immediately disambiguates it from the sibling get_vehicle_report with 'A data lookup, not a history report.' An agent can distinguish this from get_vehicle_report, list_recalls and get_fuel_economy without opening a schema.

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 trigger ('when the user gives a 17-character VIN and wants to know what car it is') plus a sample user utterance, and implies the boundary with history-report tools. It does not name the alternative tool explicitly, so routing is slightly inferential rather than fully spelled out.

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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