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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds genuine value by disclosing the response shape (totalStudies, averageSizeBytes, percentiles, ranges, largestStudies) and the underlying GET /stats/size route, which is meaningful given there is no output schema.

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 short clauses, front-loaded with the action and result, followed by the return fields and endpoint. 'Useful for understanding data volume' is mild filler but earns its place as the only usage hint.

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 parameterless aggregator with readOnlyHint already declared, the description covers everything an agent needs: scope, return fields, and the endpoint. The field enumeration compensates for the absent output schema.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline is 4 by rule. No parameter documentation is needed or missing.

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 ('Return overall study-size statistics across the registry') and enumerates the exact returned fields, which pins the tool down precisely. The registry-wide aggregate scope implicitly separates it from per-study siblings like get_study or count_studies, but no sibling is named explicitly.

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?

'Useful for understanding data volume' hints at the use case but gives no when-to-use vs. when-not guidance and never names an alternative such as count_studies or get_study_metadata. Usage is only implied.

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