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

Annotations already declare the tool as read-only and idempotent, so the bar is lower. The description adds useful behavioral context: UK market data source and that it's seller guidance, which goes beyond the annotations without contradicting them.

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 two concise sentences plus a clear example. Every element earns its place: the core purpose, the data source context, and an illustrative query. No fluff.

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?

The tool has an output schema, so return values are covered. The description provides enough context for when to use it (seller listing), the data basis (same as get_valuation), and an example. For a 3-parameter tool, this is complete.

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 has 0% description coverage, so the description must compensate. It mentions brand+model+condition and provides an example with 'good' condition, but it doesn't explain the enum values or the units/meaning of the returned price ranges. This is adequate but not fully compensating for the schema gap.

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 clearly states the tool provides suggested ask, floor, and ceiling prices for sellers, using brand+model+condition on the UK market. It also distinguishes itself from get_valuation by noting it's the same data but framed as seller guidance.

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?

It sets explicit context ('For a seller about to list') and contrasts with get_valuation, implying when to use this tool versus that alternative. However, it doesn't explicitly mention exclusions or other potential alternatives like get_price_trends.

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