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 well: it discloses the sampling basis (leading top-100 sample, results describe the sample), US-only scope, null handling for missing metrics, mean-denominator rules, and detailed billing behavior (balance must cover distinct requested market count; the check does not deduct credits; billing counts distinct returned markets; empty data costs zero). This is unusually rich disclosure beyond what any annotation would provide.

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 numbered sections (Function, Use cases, Example, Data range) front-load the purpose and the example is compact and useful. It is dense and somewhat over-long, and the date/snapshot sentence duplicates the date parameter description almost verbatim, which is wasted space.

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 complex, unannotated analytics tool this covers the essentials: scope, sampling, snapshot/date semantics, null and denominator handling, and billing. An output schema exists so return fields need not be re-explained (though they are), and the only real omission is guidance on obtaining valid category IDs and how this tool relates to its many distribution siblings.

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 every parameter (date, sampleType, categoryIds, marketplace, includeDescendantCategoryProducts) is already documented in the schema. The description reinforces sample semantics and the date/snapshot rule, but adds no syntax or meaning the schema lacks, 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 'Function' section states a specific verb and resource: select up to 100 Amazon Listings by monthly unit sales or revenue, then group them by rating-count range, and it enumerates the returned measures. This is far more precise than a generic 'distributions' tool. It never names the obvious sibling openapi_v2_markets_rating_distributions (star-rating buckets) or price/listing-age distributions, so differentiation is left implicit.

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 a clear analytical context: compare sales/revenue for Listings with few versus many ratings to assess accumulated rating volume. That is genuine usage guidance, but there are no exclusions or explicit alternatives (e.g., when to prefer rating_distributions over rating_count_distributions), so it stops short of a 5.

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