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

Goes well beyond the readOnly/non-destructive annotations: it discloses the statistical tests (Welch t, two-proportion z), the 95% confidence level, the 30-response floor for testing, the exact vsRest output values (higher/lower/not_significant/not_tested), and an explicit caution never to present not_tested as 'no difference'.

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 core operation and packs in only relevant details; the parentheticals and value enumerations earn their place, though the single dense paragraph is heavier than strictly necessary and could be split for readability.

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 enumerating vsRest outcomes and their meaning. Combined with the statistical method and sample-size floor, an agent has everything needed to call and interpret this tool 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 coverage is 80% and already documents each parameter, but the description adds real semantic value by explaining that the target question type determines the output (means + NPS on 0–10 for scale/number, per-option shares for choice/multi) and constraining the cut to acquisition source or a single-choice question.

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?

Names a specific operation (server-computed crosstab of a target question against a cut) and specifies both the target measurement types and the split dimensions, making it clearly distinguishable from siblings like prism_studies_analyze or prism_studies_segments.

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?

Explicitly says to use this 'for every "does X differ by Y?" question instead of eyeballing raw rows,' giving clear positive guidance and a stated anti-pattern. It does not name a specific alternative sibling tool to use instead, so it falls just short of the top tier.

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