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

Annotations already declare readOnly=true and destructive=false, so the safety profile is covered. The description adds real value beyond that: candidates are excluded from the response, and the non-obvious detail that reviewProgress spans every cluster while only the rows are paged. That pagination-versus-metric scope caveat is exactly the kind of behavioral context an agent cannot infer from 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 sentences, zero waste, and correctly front-loaded: purpose first, then the exclusion/alternative routing, then the paging caveat. Each sentence carries distinct information an agent needs.

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-shape burden and does so by enumerating what each cluster row contains and how members resolve. Combined with the paging caveat and sibling routing, the definition is nearly complete for a read tool, though it says nothing about result ordering or total-count signaling.

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 100%, so every parameter (limit, offset, datasetId, customerId, reviewState) is already documented in the schema. The description reinforces the paging/row concept and implies reviewState filtering but adds no format or syntax beyond what the schema provides. Baseline 3 when the schema does the heavy lifting.

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 states a specific resource (one dataset) and precisely names what it returns: the cluster roll-up with review state, decision in force, and member resolution. It explicitly distinguishes itself from sibling tools by noting candidates are excluded and pointing to list_dataset_examples and get_review_case for the other views.

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?

It names the alternatives by name and the condition selecting each: list_dataset_examples for per-example admission and get_review_case for one cluster's evidence. This is explicit when-to-use/when-to-use-something-else routing, not a vague hint.

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