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

With no annotations, the description carries the full burden and does well: it discloses the honest-empty-answer behavior, that it never estimates or imputes gaps, the observed-not-target framing, and the currency/scope caveats attached to each figure. It omits operational traits like access/permission needs or any rate or paging behavior, which keeps it short of a 5.

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?

Four sentences, front-loaded with what is returned before the caveats and the exclusion. Each sentence carries content, though the description is denser than strictly necessary and could compress the statistics list slightly.

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?

With no output schema, the description must explain the return shape, and it does: median, p25-p75 band, sample size, currency, and per-figure scope, plus the empty-result case and the data-domain limits. Nothing an agent needs to call this correctly and interpret the response appears to be missing.

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 all three parameters (category, market, metric) are already documented with examples and omission semantics; the schema even points to benchmark_categories for valid paths. The description adds no parameter-level syntax or format detail beyond that, so the baseline 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 description names the specific resource (observed price and assortment benchmarks for a product category) and enumerates the returned statistics (median, p25-p75 band, sample size, currency, scope), so an agent knows exactly what this tool produces. It also implicitly separates itself from data tools that would return conversion/revenue/AOV, which no sibling provides.

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 gives explicit exclusions: the corpus does not hold conversion rates, revenue, or AOV, and it returns an empty answer rather than estimating when data is thin. That is strong when-not guidance, but it never routes the agent to the relevant siblings (benchmark_categories for valid paths, store_audit for other analyses) inside the description itself.

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