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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)

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, and the description does not contradict them. It adds useful behavior context by specifying the output fields and limiting scope to US vehicles, going beyond what the annotations convey.

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 front-loaded with the core purpose, followed by a useful list of returned specs and example queries. It is somewhat longer than strictly necessary, but every section adds practical value for an AI agent selecting and invoking the tool.

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?

With one required parameter, rich annotations, and no output schema, the description provides enough detail about expected results and supported vehicles. It does not discuss invalid VINs or errors, but this is a minor gap for a simple read-only lookup tool.

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 already covers the single parameter vin with a clear description ('17-character Vehicle Identification Number'), so schema coverage is 100%. The description adds no additional parameter-level detail beyond what the schema provides, warranting the baseline score.

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 uses a specific verb ('Decode') and resource (VIN), and clearly describes the outcome: full vehicle specifications with a concrete list of returned fields. The scope ('Works for all US vehicles') and explicit example phrasings make it easy to distinguish from siblings like vehicle_recalls.

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 explicitly says to use this tool for VIN lookup and provides multiple natural-language examples ('decode this VIN', 'what car is this VIN?'). It lacks explicit exclusion guidance against siblings, but 'any VIN lookup' gives clear context.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

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