Skip to main content
Glama

vehicle.profile

Decode a VIN to retrieve vehicle specifications and identify open safety recalls and owner complaints for the specific make/model/year. Supports used-car due diligence and fleet safety.

Instructions

Vehicle 360 by VIN — decodes the VIN (make/model/year/trim/engine, NHTSA vPIC) then returns THAT vehicle's open safety recalls and owner complaints, keyed to the decoded make/model/year. Used-car due diligence, fleet safety, insurance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYes17-character Vehicle Identification Number.
modelYearNoOptional — disambiguates older VINs.
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses that it first decodes the VIN using NHTSA vPIC then returns recalls/complaints. It does not mention any destructive behavior, which is appropriate for a read-only tool. A small improvement could include data freshness or error handling.

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?

The description is a single, well-structured sentence that front-loads the core action and follows with use cases. No redundancy or unnecessary words.

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

Completeness4/5

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

Without an output schema, the description explains the return values (open recalls and complaints keyed to decoded data) adequately for most use cases. It could mention error handling or response structure, but the provided information is sufficient for basic selection and invocation.

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?

Schema coverage is 100%, so baseline 3. The description adds context that modelYear helps disambiguate older VINs, but does not elaborate on parameter formats or constraints beyond what the schema already provides.

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?

The description clearly states the tool decodes a VIN and returns open safety recalls and owner complaints, combining two functions. It distinguishes from sibling tools like vehicle.vin-decode (decode only) and vehicle.recalls (recalls only) by calling it 'Vehicle 360'.

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?

The description lists specific use cases: used-car due diligence, fleet safety, insurance. However, it does not explicitly state when to avoid this tool or mention alternatives (e.g., using vehicle.vin-decode if only decode is needed).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/2s-io/sdk'

If you have feedback or need assistance with the MCP directory API, please join our Discord server