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

With no annotations, the description carries the burden of explaining behavior. It discloses the data source (real DVSA MOT records, millions of tests), the age-matching methodology, and what categories of results are returned. It does not mention edge cases like unknown slugs or the no-argument listing behavior, but the schema covers listing behavior and the data provenance adds meaningful transparency.

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?

Three sentences, with the first sentence front-loading the core purpose and outputs. The second sentence adds authority and uniqueness, and the third delivers clear usage triggers. The uniqueness claim is slightly promotional but still relevant for tool selection.

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?

For a single-optional-parameter read-only lookup with no output schema, the description covers what the tool returns, the scope, the data source, and when to invoke it. It does not specify return formatting or unknown-model behavior, but these are minor given the simplicity of the tool and the complete schema.

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?

The schema fully documents the only parameter, model_slug, including an example format and no-argument listing behavior. The description aligns with this by saying 'specific used car model' but adds no parameter-level detail beyond the schema, so the baseline of 3 applies.

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 names a specific verb ('Get'), a specific resource (used car model reliability in the UK), and concrete outputs: common faults, best/worst years, and DVSA MOT first-time pass rates. This clearly differentiates it from siblings focused on tax, MOT due dates, running costs, price guides, and car search.

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 states when to use the tool: when someone asks whether a model is reliable, what goes wrong, which year to buy or avoid, or what to check before buying. It does not explicitly name alternatives or exclusions, but the sibling names and the focused reliability context make the intended use clear.

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