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

With no annotations, the description is the only behavioral disclosure. It says what the response contains (scores, average, delta), but it does not disclose operational traits such as default ordering, pagination, or whether it is non-mutating. No contradiction with annotations exists because none are present.

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 first sentence front-loads the core purpose and is appropriately short. However, the 'UNDERSTANDING —' prefix and the governance/market metaphor add style and emphasis rather than new operational detail, so the description is not as information-dense as it could be.

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?

There is no output schema and no annotations, so the description must carry the full context; it leaves parameter semantics, pagination behavior, and the relationship to related cohort endpoints unresolved. An agent could guess the required inputs from the schema, but the description alone is not sufficient to use the tool confidently among its 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 coverage is only 20% (only `context` has a description), and the tool description does not explain `kind`, `slug`, `page`, or `limit`. The reference to a cohort is abstract and is not linked to the required parameters, so the description does not compensate for the low schema coverage.

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 clearly identifies the deliverable: facet-level scores for each cohort member, with the cohort average and delta. It conveys the resource ('cohort') and the comparative nature, but it does not explicitly contrast it with near-sibling tools like cohort_rankings or cohort_stats.

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 '60 in governance... market sits at 45' example implies when the tool is valuable (benchmarking an individual score against the group), giving some usage context. It stops short of explicit when-to-use/when-not-to-use guidance, and no alternative tools are named.

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