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LiveDataLink

by io.github.blackboxfoundry

vin_decode

Read-onlyIdempotent

Decode a Vehicle Identification Number (VIN) to get full vehicle specifications. Returns year, make, model, trim, body style, engine specs (cylinders, displacement, HP), drivetrain, transmission, fuel type, doors, manufacturer, and assembly plant location. Use this for 'decode this VIN', 'what car is this VIN?', 'look up a VIN number', 'what are the specs on this vehicle?', 'identify this car', or any VIN lookup. Works for all US vehicles - cars, trucks, SUVs, motorcycles, trailers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYes17-character Vehicle Identification Number (VIN)

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral context by listing the exact fields returned and the scope of supported vehicles (all US vehicles including motorcycles and trailers).

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 efficient and front-loaded: it states the core function, then lists outputs, then gives query examples, then scope. Every sentence adds useful information, and the example phrases are practical for an agent matching user intent.

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

Completeness5/5

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

For a tool with one well-documented parameter, strong annotations, and no output schema, the description is complete: it explains what the tool returns, the vehicle types covered, and the user phrasings that should trigger it. No critical selection or invocation information is missing.

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 schema already fully documents the single 'vin' parameter, including its 17-character format. The description adds no additional parameter-level semantics beyond restating 'VIN', so the baseline score of 3 is appropriate given 100% schema coverage.

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 a specific action ('Decode a VIN') and resource, and enumerates the returned vehicle attributes. It does not explicitly distinguish itself from the sibling 'vehicle_recalls' tool, though the specifications-focused output makes the difference mostly implicit.

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 provides concrete example queries and says to use it for 'any VIN lookup', giving an agent clear trigger phrases. However, it does not state when not to use it, such as when the user is asking about recalls rather than vehicle specifications.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

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