Crypto, web, finance and app data tools

VIN Decoder

vin_decode

Decode a vehicle VIN (cars, trucks, motorcycles sold in the US): make, model, year, trim, body, engine, fuel and electrification, drive, transmission, plant and safety equipment, with a validity check. Source: NHTSA vPIC.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "vin": "<vin>"
    },
    "name": "vin_decode"
  }
}

Input Schema

Name	Required	Description	Default
`vin`	Yes
`model_year`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 26, 2026

TDQS

B3/5.0

Behavior3/5

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

With no annotations, the description must carry the behavioral burden. It adds useful context: the data source (NHTSA vPIC), the US-market scope, and the presence of a validity check. However, it does not state what happens with invalid or non-US VINs, error behavior, or how the optional model_year affects decoding.

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?

The description is a single dense sentence that front-loads the core purpose and lists outputs efficiently. It is appropriately sized for a decode operation, though the long field enumeration may be slightly overpacked.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so the description need not explain return values in depth. The US-market scope and source are helpful, but the undocumented optional model_year parameter and absent error/invalid-VIN behavior leave meaningful gaps for an agent calling this tool.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, but it only indirectly identifies the vin parameter. The optional model_year parameter is not mentioned at all, leaving the agent without guidance on when or why to supply it, which matters for ambiguous VIN year characters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb (decode) and resource (vehicle VIN) and enumerates the exact fields returned, so an agent can understand the tool's function without ambiguity. It does not explicitly distinguish itself from the sibling vehicle_recalls tool, which is its closest domain-adjacent alternative.

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

Usage Guidelines2/5

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

The description implies usage by saying it decodes a VIN for US-market cars, trucks, and motorcycles, but it provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or conditions such as non-US VINs or when to prefer vehicle_recalls instead.

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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