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

Goes well beyond the read-only annotations: it discloses the test statistics (Welch t / two-proportion z) at 95%, the 30-response floor, and the exact vsRest value set including not_tested. The explicit warning never to present not_tested as 'no difference' is high-value interpretive guidance an agent could not infer from schema or annotations.

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 core capability is front-loaded and every clause carries substance (output shapes, test types, floor, interpretation rule) rather than filler. It is dense and somewhat run-on, but no sentence is wasted.

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 by naming the vsRest values and the statistics produced. Gaps remain around studyId semantics and what happens if cutBySource and cutByQuestionIndex are both supplied or both omitted.

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 80%, so the baseline is 3. The description clarifies that the cut comes from acquisition source or a single-choice question, reinforcing cutBySource/cutByQuestionIndex, but adds no syntax or format detail beyond the schema and is silent on the required studyId and on conflict between the two cut parameters.

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 verb and resource ('server-computed banner cut with significance') and details exactly what a target question yields for scale/number vs choice/multi. It never names a sibling such as prism_studies_segments_compute, so differentiation from the other prism_studies_* analysis tools 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?

Gives a clear when-to-use trigger ('Use this for every "does X differ by Y?" question instead of eyeballing raw rows'), which is stronger than implied usage. It does not, however, name an alternative tool or state when a different prism_studies analysis call would be preferred.

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