check_vehicleFree DVLA-backed check of a UK vehicle from its number plate: London ULEZ compliance, Clean Air Zone compliance, Scottish LEZ, MOT status and road tax status, plus make/colour confirmation. Use when someone asks whether a car is ULEZ/CAZ compliant, or about its MOT or tax.
| Name | Required | Description | Default |
|---|---|---|---|
| vrn | Yes | UK number plate (VRN), e.g. KM71AEA. Spaces are fine. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description states 'Free DVLA-backed check', confirming it is a safe read. The description adds behavioral context by listing the specific checks performed (ULEZ, CAZ, LEZ, MOT, tax, make/colour), which goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core functionality and followed by usage guidance. No wasted words; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter and no output schema, the description provides a comprehensive list of what the tool returns (ULEZ, CAZ, LEZ, MOT, tax, make/colour). This fully informs the agent of the tool's capabilities.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema describes the single parameter vrn with an example and note about spaces. The description mentions 'from its number plate' but adds no additional meaning or format detail beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a 'Free DVLA-backed check of a UK vehicle from its number plate' and lists specific outputs (ULEZ, CAZ, LEZ, MOT, tax, make/colour). It distinguishes from siblings (charge_info, list_charges) which are about charging, not vehicle checks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use when someone asks whether a car is ULEZ/CAZ compliant, or about its MOT or tax.' This provides clear guidance on when to use. It does not mention when not to use, but the use cases are well-defined and the sibling tools cover different domains.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: list_charges for discovering charges, charge_info for detailed info on a specific charge, and check_vehicle for vehicle compliance. No functional overlap.
All tool names follow a consistent verb_noun pattern in snake_case (list_charges, charge_info, check_vehicle), making predictable.
Three tools are appropriate for the UK toll and vehicle check domain—covering discovery, details, and vehicle checks without unnecessary duplication.
The set covers the main needs: listing charges, getting details, and checking vehicle compliance. Could potentially include a tool for payment history, but overall solid for the stated purpose.