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

Beyond the annotations (readOnlyHint=false, destructiveHint=true), the description discloses the algorithm, the overwrite consequence of recomputing, that naming consumes inference credit, and that the call may return a `needs_confirmation` state. These are exactly the behavioral traits an agent needs before invoking an expensive, mutating operation, and none are recoverable from the structured fields.

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?

Three dense sentences, front-loaded with the core action, then preconditions, then cost and confirmation caveats. Every clause carries information; there is no filler.

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?

For a tool with no output schema, the description covers inputs preconditions, mutation semantics, cost, and a possible confirmation return. It could say more about what the discovered segments/names look like on return and how approvalId relates to confirmation, but overall it is complete enough to call 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 only 33%: studyId and approvalId carry no descriptions. The description adds no parameter-level meaning, and notably leaves approvalId unexplained even though it ties directly to the `needs_confirmation` flow it mentions. With low coverage, the description should compensate here and 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 gives a specific verb and resource: it runs k-means over the study's numeric answers with silhouette-picked k and names the resulting segments. This is far more than a restatement of the name. It does not, however, explicitly distinguish itself from the sibling prism_studies_segments (the likely read/list counterpart), so an agent must infer the compute-vs-read split from the name alone.

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?

Clear preconditions are stated: 30+ responses and 2+ numeric questions are required, and recompute overwrites existing results. That is real when-to-use context. It stops short of naming an alternative tool or an explicit when-not-to-use case, so it lands just below the top band.

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