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

Annotations only declare readOnlyHint and openWorldHint, but the description goes well beyond: it discloses a guaranteed `coverage` block, `enrichment_depth` semantics, the ~42-point catalog_gap span, the caveat that cohort numbers partly reflect coverage rather than the market, and the pricing tier ($0.05 B2, included in Understanding/Influence, see apis://prices). This is exactly the extra operational context annotations cannot carry.

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?

Front-loads the purpose before the caveats and pricing, and the coverage warning is high-value rather than filler. It is dense and long, with parenthetical detail (~42 points of catalog_gap) that is borderline for a tool description, but nearly every clause carries substance.

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 does the work of describing the returned sheet (statistics, coverage block, enrichment_depth, adoption rates) and even adds pricing. It is largely self-sufficient for calling correctly; the only missing piece is explicit routing among the cohort_* 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% and each of the three parameters (kind enum, slug, context) is already documented in the schema. The description adds no extra meaning about kind/slug syntax or the enum values, 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?

States a specific resource (the market-stats sheet / distribution for a whole market) and enumerates exactly what it returns: Kin Score mean/median/min/max/stdev, agent-readiness spread, band split, per-facet averages, artifact adoption. It is clearly a distribution-statistics tool rather than a ranking or listing one. However, it never names a sibling (cohort_scores, cohort_rankings, cohort_capabilities) as the alternative, so differentiation from close neighbors 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?

Frames itself as the 'UNDERSTANDING' layer and tells the agent to read the coverage block before quoting the mean, which implies when the output is trustworthy. But it gives no explicit when-not or alternative-tool routing against the many cohort_* siblings, so selection guidance is implied rather than stated.

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