list_review_statistics

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered by structured data. The description adds only the vague scoping notion of 'studied subjects' and says nothing about pagination behavior (page_after_id/page_before_id are present in the schema) or how filtering thresholds interact.

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 single sentence is front-loaded and wastes no words, but its brevity stems from under-specification rather than disciplined compression; there is no structure to speak of.

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 8 undocumented parameters, no output schema, and no required fields, the description leaves too much to inference for an agent to construct a correct query or interpret the response. It should at minimum explain the filter semantics and pagination contract.

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 0% across 8 parameters, and the description names none of them. It never explains ids vs subject_ids, the subject_types enum values, updated_after, or the percentages_less_than/greater_than pair, so an agent has no textual guidance for any filter.

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 verb 'List' and the resource 'accuracy statistics' are identifiable, but 'accuracy statistics for studied subjects' leaves unclear exactly what is returned (per-subject accuracy percentages? per-review breakdowns?). It also draws no boundary against siblings such as list_reviews or get_summary, which could plausibly surface related data.

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?

There is no statement of when to use this tool instead of list_reviews, get_summary, or list_subjects, and no prerequisites or exclusions. The only implicit guidance is the phrase 'for studied subjects', which hints that unstudied subjects are excluded but never says so explicitly.

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