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

The readOnlyHint annotation is reinforced rather than contradicted. The description goes further by disclosing that membership changes over time, that results are means over members present on each date, and that movements cannot be attributed to scores alone without considering population change. It also adds pricing/plan context.

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 description is dense but front-loaded with the core definition. The narrative about the missing middle and pricing information both earn their place by aiding tool selection and cost awareness, though the text is slightly longer than strictly necessary.

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?

The description covers output, caveats, and read-only behavior well. However, the required kind and slug parameters remain under-specified and there is no output schema to clarify them further. An agent can probably infer usage from the example, but the definition is not fully self-sufficient for correct invocation.

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?

Only the optional context parameter is described in the schema. The required kind and slug parameters are not explained — the description only hints at slug through the 'banking' example and never defines how the enum values map to cohorts. With schema description coverage at 33%, the description needed to compensate but largely does not.

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 states exactly what is returned — a set-level time series of Kin Score and agent readiness, with the mean over members present per date and members_scored. It also differentiates itself from per-provider and whole-dataset series, so it is clearly distinguishable from siblings like cohort_scores or get_rating_history.

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 description gives a concrete motivating use case ('how has banking moved over six months') and frames the tool as the missing middle between per-provider and whole-dataset series. It does not explicitly name sibling tools or state when not to use it, so it falls just short of full guidance.

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