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Check a UK vehicle registration (tax and MOT)

vehicle_lookup
Read-onlyIdempotent

Look up a UK vehicle registration (number plate) against live DVLA records and return its tax status and due date, MOT status and expiry, make, colour, fuel type, engine size, CO2, Euro emissions status and year of manufacture. Use this whenever somebody asks whether a vehicle is taxed, has a valid MOT, or wants its details from a registration such as CV23 KWR.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesJSON request body for this check.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare this read-only, idempotent and non-destructive, so safety is covered. The description adds the data source and freshness ('live DVLA records'), which is useful, but says nothing about failure modes (not found, malformed/foreign plate), auth requirements, or rate/quota limits. Note a mild tension between 'live DVLA records' and openWorldHint=false.

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?

Two sentences, capability first and usage trigger second, with no filler or throat-clearing. The long comma-delimited return-field enumeration is dense but every item is informative, so it earns its space.

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 no output schema, the description carries the return-value burden explicitly by listing the tax/MOT statuses and vehicle attributes, which is exactly what an agent needs. Remaining gaps are minor: no mention of what happens on an unknown plate, and no disambiguation from the sibling vehicle check tools.

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?

There is a single parameter with 100% schema description coverage, so the schema already fully documents 'vrm' including the same 'CV23 KWR' example the description repeats. Baseline 3 applies; the description adds no new syntactic or formatting constraints beyond the schema.

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?

States a specific verb and resource ('Look up a UK vehicle registration ... against live DVLA records') and enumerates exactly what comes back (tax, MOT, make, colour, fuel, engine size, CO2, emissions, year). It never distinguishes itself from close siblings such as vehicle_check_road_legal, vehicle_check_fleet or vehicle_check_clean_air, which an agent could reasonably confuse it with.

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 second sentence gives concrete triggers: 'whenever somebody asks whether a vehicle is taxed, has a valid MOT, or wants its details from a registration'. That is clear when-to-use guidance, but there are no exclusions or routing hints toward the sibling vehicle_* tools.

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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