VIN decoding and European vehicle data for automotive workflows.
Decode a Vehicle Identification Number (VIN) to get make, model, year and other vehicle details.
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | 17-character Vehicle Identification Number |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description alone must convey behavioral traits. It only states the intended output but does not mention error handling, validation, rate limits, or any side effects. This is insufficient for full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler words. It efficiently conveys the purpose and output range. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter lookup tool with no output schema or siblings, the description is largely complete. It could mention handling of invalid VINs, but the current level is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the only parameter 'vin'. The description restates the character length already in the schema, adding no new meaning. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Decode' and the resource 'Vehicle Identification Number (VIN)', and explicitly lists the output (make, model, year, other details). No ambiguity or irrelevant information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool, prerequisites, or alternatives. Since there are no sibling tools, implicit usage is assumed but not stated. A score of 2 reflects the lack of explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
decode_vinClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Only one tool exists, so there is no possibility of confusion between tools.
Single tool name 'decode_vin' is clear and follows a verb_noun pattern; consistency is inherent.
One tool is borderline—sufficient for a focused VIN decoding service, but feels thin for broader vehicle data tasks.
Decoding is fully covered, but missing related operations like validation or batch decoding are minor gaps.
