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

With no annotations, the description carries the full burden and does so thoroughly: US-only scope, per-category independence, null handling, snapshot-date rules (unpublished date returns no data), a pre-query balance check that does not deduct credits, billing by distinct returned markets with empty data costing zero, and per-mean denominator caveats. These are exactly the behavioral traits an agent could not infer otherwise.

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?

Well organized into Function / Use cases / Example / Data range with the purpose front-loaded, and the example is compact. It costs some points for repetition, notably the date/snapshot rule which duplicates the schema, and a dense billing sentence that could be tightened.

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 5-parameter, output-schema-backed analytics tool, the description covers everything an agent needs: scope, snapshot semantics, cost/billing behavior, and metric-specific caveats like excluding null-metric listings while counting zeros. An output schema exists, so the absence of return-format detail is not a gap.

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 100%, so the schema already documents every parameter; baseline is 3. The description's example JSON and date guidance largely restate the schema descriptions (the date rule is repeated almost verbatim), adding little beyond what the structured fields already provide.

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 'Function' section states a specific verb and resource: select up to 100 Amazon Listings by monthly unit sales or revenue, then group by star-rating range. The grouping dimension (star rating) clearly differentiates it from the sibling openapi_v2_markets_rating_count_distributions, and it also enumerates the exact returned metrics.

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 'Use cases' section gives clear context (profiling customer-rating patterns, assessing demand distribution in the leading sample), and the data-range text routes the caller to the resolvedDate from market search. It does not, however, state when NOT to use this tool or name a sibling to use instead when the question is about rating counts rather than star-rating bands.

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