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

Annotations already declare readOnly, idempotent and non-open-world, so the safety profile is covered. The description adds real behavioral context beyond that: a hard cap of 10 matches and the exact fields returned (MOT reliability grade, first-time pass rate, share of UK cars beaten).

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?

One sentence, front-loaded with the lookup purpose and examples, ending with the downstream consumer. No filler and nothing an agent needs is buried.

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?

With no output schema, the description carries the burden of describing results and does so fully (count cap plus the three returned metrics). Scope (UK), input format, and next step are all present for a single-parameter tool.

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%, so baseline is 3, but the description earns an extra point by illustrating the accepted query shapes — bare model ("Fiesta"), make plus model ("Toyota Yaris"), and make alone ("Kia") — which clarifies free-text flexibility the schema's 'Make and/or model name' only hints at.

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?

States a specific verb (find) and resource (UK car makes and models), supplies concrete query examples, and distinguishes itself from the reliability siblings by framing its output as input to get_model_reliability or get_make_reliability.

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 closing clause makes the workflow explicit: this is the discovery step whose results feed the two reliability tools. It does not state exclusions (e.g. when to use lookup_plate instead), but the intended sequencing is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.