Vin Decode
vin_decodeDecode a VIN and return detailed information about the vehicle.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
vehicle-data
by com.vincario
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral transparency. It only mentions that it returns detailed information but doesn't describe error handling, data format, or any side effects, which is insufficient for a tool that processes potentially malformed input.
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 sentence with no wasted words, making it easy to parse. However, this brevity comes at the cost of necessary detail, but for conciseness dimension, it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of sibling tools and the lack of annotations, the description is incomplete. It doesn't clarify what sets it apart from vin_decode_info, nor does it explain the output structure or any prerequisites.
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?
The input schema has one parameter with no description (0% coverage), so the description must compensate. It merely restates that the tool decodes a VIN, which doesn't add meaningful detail about the expected format, constraints, or validation rules.
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 clearly states the tool decodes a VIN and returns vehicle information, using a specific verb and resource. However, it doesn't differentiate from the sibling tool vin_decode_info, which may have overlapping functionality.
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?
No guidance is provided about when to use this tool versus alternatives like stolen_check or vehicle_market_value. The description alone gives no context for selection.
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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TDQS
A3.5/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: stolen check, market value, VIN decoding, and field listing. Even vin_decode and vin_decode_info are separate since one returns decoded data and the other returns available fields.
Naming Consistency4/5
All tool names use lower_snake_case and are descriptive, but they mix noun-phrase patterns (vehicle_market_value) with verb-noun patterns (vin_decode). This is a minor inconsistency, though still readable and predictable.
Tool Count5/5
Four tools is a well-scoped size for a vehicle data API, covering the essential VIN-related lookups without unnecessary redundancy or overwhelming breadth.
Completeness4/5
The surface covers core VIN tasks: decode, market value, stolen status, and field availability. Minor gaps like VIN validation or vehicle history reports exist but are not obvious dead ends for common use cases.