UK Data for Agents

vehicle_problem_counts

Read-onlyIdempotent

How many complaints, recalls and federal defect investigations does this model have? Returns the three counts NHTSA links to a make, model and model year, as a quick screen when comparing cars. Source: NHTSA NCAP records. Counts are not adjusted for sales volume, so a popular model will show more; only models in the NCAP programme are covered. Use vehicle_recalls and vehicle_complaints for the detail. (cost: 1 credit)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "vehicle_problem_counts"
  }
}

Input Schema

Name	Required	Description
`make`	No	string, required, vehicle make, e.g. honda
`model`	No	string, required, US model name, e.g. accord
`modelYear`	No	integer, required, model year, e.g. 2019

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 5, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so safety is covered. The description still adds real behavioral context the annotations do not: the NCAP source, the sales-volume caveat, the coverage restriction to NCAP-programme models, and the 1-credit cost. It does not describe the return shape beyond 'three counts', but the added caveats are substantive.

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?

Front-loads the core question, then layers scope, caveats, alternatives, and cost in a logical order with no filler. The rhetorical opening sentence is slightly indirect compared with a declarative statement, but nothing is wasted.

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?

There is no output schema, and the description compensates by naming the three returned counts. It also closes the two gaps an agent would otherwise hit: sales-volume skew and NCAP-only coverage, plus the sibling routing.

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?

Schema coverage is 100% and each parameter already documents itself with an example, so the description need not restate syntax. The description only implies that make, model, and model year jointly scope the counts, which is baseline for full schema coverage.

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 resource and verb set: returns complaint, recall, and federal defect investigation counts for a make/model/model year. It explicitly names what it is not (the detail-level tools vehicle_recalls and vehicle_complaints), so an agent can separate it from its nearest siblings without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a concrete use case ('quick screen when comparing cars') and names the alternatives for drill-down ('Use vehicle_recalls and vehicle_complaints for the detail'), which is an explicit when-to-use and when-to-switch-away instruction.

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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