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MCPFax Public-Data Utility API

VIN decode

v1_vin_decode
Read-onlyIdempotent

VIN decode: Decode a US-market Vehicle Identification Number. Source: NHTSA vPIC. $0.01 per call · GET /v1/vin-decode

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYes11-17 char VIN. Example: '1HGCM82633A004352'.
yearNoModel year hint. Example: '2003'.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / vin / examples
      Added value: +[
      +  "1HGCM82633A004352"
      +]
    • addedInput schema / properties / year / examples
      Added value: +[
      +  2003
      +]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable operational context beyond those annotations: external source (NHTSA vPIC), cost ($0.01 per call), and HTTP method (GET). This helps an agent understand dependencies and side effects.

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 compact single line that front-loads the core purpose before adding source and pricing details. The opening 'VIN decode' is slightly redundant with the title, but otherwise every element earns its place.

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?

For a simple two-parameter read-only endpoint, the description provides essential context: purpose, market scope, source, price, and endpoint. It omits return-value details, but the absence of an output schema and the straightforward decode operation make the definition sufficient for correct 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?

The input schema fully documents both parameters with 100% coverage, including vin format/example and year as a model-year hint. The description adds no additional parameter-level meaning, so the baseline of 3 is appropriate.

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?

States a specific verb ('Decode') and a specific resource ('US-market Vehicle Identification Number'), and adds the data source (NHTSA vPIC). No sibling tool overlaps with VIN decoding, so the purpose is unambiguous and the tool stands out at a glance.

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 clearly sets the target scope ('US-market') and the operation (decode), which tells an agent when this tool applies. It does not explicitly name alternatives or exclusion conditions, but the unique domain and stated source give sufficient 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

A3.7/5.0
Disambiguation5/5

Each tool maps to a distinct public data source and lookup type, from weather and geocoding to legal codes and vehicle VINs. Even adjacent tools like weather vs. weather_alerts or geocode vs. reverse_geocode are clearly separated by resource and direction.

Naming Consistency4/5

All tools share a consistent v1_ prefix and snake_case resource naming, making the pattern predictable. Minor inconsistencies exist: most names are noun phrases (v1_country, v1_stock_quote) while a few are verb-led (v1_geocode, v1_validate_email, v1_unit_convert), and some abbreviations like v1_cfr and v1_lei are less descriptive.

Tool Count2/5

At 31 tools, the surface is large and will strain agent tool-selection, even though each tool is individually useful. The broad 'public data utility' scope explains the count, but the calibration threshold of 25+ tools indicates an oversized set for practical use.

Completeness4/5

For a lookup-oriented public data utility, the set covers a wide range of common needs—weather, finance, location, legal/medical codes, domain/network, and conversions—without dead ends. It lacks some obvious public data categories (e.g., web search, population/census, news) and enumeration endpoints, but agents can work around these gaps.

Resources