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

With no annotations, the description carries the full burden and discharges it well: it discloses the up-to-100 sampled nature, that missing metrics stay null, that an unpublished explicit date returns no data, the resolvedDate reuse rule, a pre-query balance requirement, and that billing counts distinct returned markets while empty data is free. It also explains that means exclude null-metric listings and may have different denominators. Only the return shape and pagination semantics are left unaddressed, which the output schema covers.

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?

Numbered sections (Function, Use cases, Example, Data range) front-load the core behavior, and the example concretizes invocation. Section 4 is dense, packing sample, date, billing and null-handling details into one block, but each sentence carries operational value for this complex tool.

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?

An output schema exists, so return values need no explanation, and the description covers the non-obvious traits an agent needs: sampling, null policy, date/resolvedDate behavior, and billing. The main gap is that it never routes the agent among the many sibling markets_*_distributions tools, which matters given how similar their names are.

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 each parameter thoroughly, setting the baseline at 3. The description adds marginal value: the example shows categoryIds/sampleType pairing, and 'Data range' clarifies the US-only marketplace and descendant inclusion. Nothing about parameter syntax or formats is added beyond the schema.

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 Listings by monthly unit sales or revenue, then group by A+/video configuration and return an A+ aggregate. This distinguishes it from sibling distribution tools (price, rating, seller, listing-age) by its content-configuration focus. The output composition (counts, shares, per-listing means) is named explicitly.

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?

A dedicated 'Use cases' section says to use it to assess A+ and video adoption and compare sales/revenue across content groups for PDP planning. That gives clear context for selection but does not name alternatives (e.g., the other markets_*_distributions tools) or state when-not to use it, so it stops short of a full routing rule.

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