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

Annotations already establish that this is a read-only, idempotent, non-destructive, non-open-world operation. The description adds meaningful behavioral context beyond that: the data source is Cyclesite's measured UK used-price data, and the ranking metric is percent retained vs new RRP.

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 front-loaded with the core behavior, then the return shape, then a concrete example. It is three compact sentences with no redundant or filler content.

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 simple read-only ranking tool with an output schema and safety annotations, the description supplies the essential context: data source, metric, and top-N behavior. Minor gaps around explicit sort semantics remain, but the agent has enough to call it 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 description coverage is 50%: limit is documented in the schema, while sort has only enum values 'best' and 'worst'. The description adds the meaning of 'top N' and the retention metric, but it does not explicitly describe the sort parameter's effect or defaults, so it is adequate rather than rich.

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 specifically states that the tool ranks brands by value retention using measured UK used-price data and returns top N brands by percent retained vs new RRP. This is clear and distinct in practice, but it does not explicitly differentiate itself from siblings such as get_price_trends or get_valuation.

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 example question ('which bike brands hold their value best?') implies the intended use case. However, the description gives no explicit when-not-to-use guidance or alternatives, so the agent must infer competitive fit against sibling tools.

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