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

Annotations already declare readOnly/idempotent/non-destructive, yet the description adds substantial extra context: data provenance and licence (DVSA, Open Government Licence), dataset scale and coverage window (4876 recalls, 2016→2027, 272 makes), case-insensitive and alias normalization behaviour, and the explicit exclusion of VIN-level lookups. This is rich behavioural disclosure beyond structured fields.

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?

Dense but front-loaded — the core purpose and return fields come first, then normalization and scope notes. Slightly overpacked with parenthetical detail, but nearly every clause carries information an agent needs.

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 compensates by enumerating the returned fields and linking them to a GOV.UK checker. Combined with the coverage window and scope exclusions, an agent has everything needed to call this correctly.

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 the baseline is 3, but the description adds real meaning beyond the schema: it documents normalization ('Vauxhall Corsa', 'vauxhall corsa', 'VAUXHALL CORSA' resolve identically; 'Mercedes-Benz', 'VW', 'Land Rover' understood) and the effect of omitting model. The 'since' parameter is only indirectly covered via the launch-date window.

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+resource: 'Official UK vehicle safety recalls (DVSA...)' for a make and model, and enumerates the exact fields returned (recall number, launch date, concern, defect, remedy, build-date range, vehicles affected, GOV.UK link). It is immediately distinguishable from sibling tools like uk_mot_reliability and uk_mot_vehicle_search by domain.

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?

Gives clear conditional guidance: 'Omitting model lists the make's recalls and model names' and bounds the tool's scope ('not individual VINs'). However, it never names or contrasts the closest sibling, uk_recalls_search, so the agent must infer which recall tool to pick.

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