vin-recall-mcp
Server Details
Decodes US VINs and looks up open NHTSA safety recall campaigns for a vehicle.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pratikmehkarkar/vin-recall-mcp
- GitHub Stars
- 0
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Tool Definition Quality
Average 4.4/5 across 2 of 2 tools scored.
Each tool has a distinct purpose: one decodes a VIN into vehicle details, the other checks recall campaigns. There is no ambiguity between them.
Both tool names use a consistent verb_noun pattern in snake_case ('check_recalls', 'decode_vin'), making them predictable.
Two tools are appropriate for the narrow domain of VIN decoding and recall lookup. No unnecessary tools exist.
The tools cover the primary use cases but lack a way to check if a recall was actually repaired on a specific VIN, which is a minor gap.
Available Tools
2 toolscheck_recallsCheck Vehicle RecallsARead-onlyIdempotentInspect
List open safety recall campaigns for a vehicle by VIN, using the NHTSA Recalls database. Decodes the VIN internally to get make/model/year, then looks up campaigns for that vehicle configuration. Important: results are recall campaigns issued for the make/model/year configuration — this does NOT confirm whether this specific VIN's vehicle was actually repaired at a dealer. Always describe results as 'open recall campaigns for this vehicle,' never as whether the car 'is' or 'isn't' fixed.
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | A 17-character US vehicle VIN (letters and digits, excluding I, O, and Q). The VIN is decoded internally to determine make/model/year for the recall lookup. |
Output Schema
| Name | Required | Description |
|---|---|---|
| vin | Yes | |
| make | No | |
| note | No | |
| year | No | |
| model | No | |
| checked | Yes | |
| recalls | No | |
| summary | Yes | |
| recallCount | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about internal VIN decoding, the source (NHTSA), and the important behavioral nuance that results apply to the vehicle configuration, not the specific VIN's repair status. No contradiction with annotations.
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 extremely concise: three sentences total. The first sentence states purpose, the second explains internal processing, the third provides critical usage guidance. No wasted words, well-structured.
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?
Given the tool has only one parameter, rich annotations, and an output schema (present but not shown), the description is complete. It covers purpose, process, source, and result interpretation. No missing information.
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?
The input schema has 100% description coverage for the 'vin' parameter, but the description adds value by specifying the VIN format (17 characters, excluding I/O/Q) and explaining that it is decoded internally. This goes beyond the schema's basic description.
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 tool lists open safety recall campaigns for a vehicle by VIN using the NHTSA database. It provides a specific verb ('list'), resource ('open safety recall campaigns'), and scope ('by VIN'). It distinguishes itself from the sibling tool 'decode_vin' which only decodes VINs.
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?
The description explains when to use the tool (to check recall campaigns by VIN) and provides important guidance on how to interpret results (not to say the car is fixed). While it does not explicitly state when not to use it, the guidance on describing results is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
decode_vinDecode VINARead-onlyIdempotentInspect
Decode a 17-character US vehicle VIN into make, model, year, body class, engine cylinders, fuel type, drive type, and assembly plant country, using the NHTSA vPIC database. Covers US-market vehicles built from 1981 onward (the 17-character VIN standard); pre-1981 and some foreign-market vehicles may return partial or no data. Decoded fields are manufacturer-submitted and individual fields may be missing even for a successfully decoded VIN.
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | A 17-character US vehicle VIN (letters and digits, excluding I, O, and Q). |
Output Schema
| Name | Required | Description |
|---|---|---|
| vin | Yes | |
| make | No | |
| note | No | |
| year | No | |
| found | Yes | |
| model | No | |
| summary | Yes | |
| fuelType | No | |
| bodyClass | No | |
| driveType | No | |
| plantCountry | No | |
| engineCylinders | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond the annotations: it notes that decoded fields are manufacturer-submitted and may be missing, and that the tool may return partial data for certain vehicles. This complements the readOnlyHint, openWorldHint, and idempotentHint annotations.
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 three well-structured sentences. The first sentence states the core action and outputs, the second provides coverage limitations, and the third adds data completeness caveats. Each sentence adds value without superfluous content.
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?
Given the presence of an output schema (not fully shown) and annotations, the description provides all necessary context: purpose, usage guidelines, behavioral traits, and parameter details. The sibling tool is clear, and no critical information is missing.
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%, with the 'vin' parameter described in the schema. The description repeats the 17-character requirement but adds no additional semantic information beyond what the schema already provides.
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 explicitly states the verb 'Decode', the resource 'VIN', and lists specific output fields (make, model, year, etc.). It also mentions the data source (NHTSA vPIC), clearly distinguishing it from the sibling tool 'check_recalls' which handles recalls.
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?
The description provides clear usage context: it covers US-market vehicles built from 1981 onward, and warns that pre-1981 or foreign-market vehicles may return partial or no data. It does not explicitly mention when to use this tool over the sibling, but the differentiation is implied by the tool's purpose.
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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