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

Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is not the description's burden. What the description does add is the shape of the payload (counts, breakdowns, ranges, coverage), which matters because no output schema exists. It says nothing about caching, snapshot freshness, or how missing/unknown values are represented, but the core disclosure is present.

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?

A single sentence fragment with no filler, front-loading the highest-level characterization ('size and makeup') before the itemized contents. Nothing could be cut without losing information.

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 zero-param, read-only summary tool with no output schema, the description covers the main return buckets well enough to decide whether the result is useful. Terms like 'field coverage' and 'makeup' remain undefined, so the agent still cannot predict exact field names or value formats.

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?

Zero parameters, so there is nothing for the description to disambiguate; the schema is empty and coverage is moot. Baseline 4 applies for a parameterless tool.

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 enumerates the concrete contents of the resource — total studies, breakdown by subject and design, year range, field coverage — which is far more informative than the tautological title 'Database stats'. It lacks an explicit verb ('returns') and does not distinguish itself from the overlapping sibling aggregate_studies, which could also produce breakdowns.

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 when-to-use guidance, no prerequisites, and no named alternative. Given that aggregate_studies sits right next to it and plausibly produces similar breakdowns, the absence of any routing hint leaves the agent to guess which tool to call for corpus-level numbers.

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