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

Goes well beyond the readOnly annotation: it discloses the return shape (MEAN over members present each date plus members_scored), the attribution caveat (membership shifts as the catalog grows), and full pricing/plan details ($0.05 B2, included in Understanding and Influence, apis://prices). This is exactly the extra context annotations can't 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?

Front-loaded with the core purpose, then returns and pricing. Mostly earns its length, though the editorial aside ('a move you cannot attribute to scores rather than population is not a finding') is slightly discursive for a tool definition.

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 carries the return-value burden and does so (MEAN and members_scored), plus pricing and the key membership caveat. An agent has everything needed to call it correctly.

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 kind/slug/context are already documented and the description's cohort framing adds little param-level detail beyond what the schema states. Baseline 3 applies when the schema carries parameter semantics.

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?

Opens with a specific framing — 'the SET-LEVEL time series' of Kin Score and agent readiness — and explicitly positions it against the per-provider series and whole-dataset licence it sits between, so an agent can tell what it returns. The verb is descriptive rather than a crisp action verb, and it doesn't name the sibling tools (e.g. cohort_scores, get_rating_history) directly, keeping it from a 5.

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 the use case concretely ('how has banking moved over six months had no answer') and contrasts it with the per-provider and whole-dataset alternatives, giving clear context for selection. It stops short of naming the actual sibling tools or stating explicit exclusions, so it's a strong 4 rather than a 5.

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