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

Annotations already cover the safety profile (readOnly, non-destructive, closed-world). The description adds a genuine behavioral fact beyond them: unknown values are counted separately, which materially affects how an agent interprets bucket results. It says nothing about bucket ordering or the default top limit, but the schema carries those.

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, front-loaded with the core action and grouping facets, then the use case. 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?

With no output schema, the description does reasonably well by implying a bucket-to-count result shape and explaining unknown-value handling. Minor gaps remain around what a bucket looks like and default limiting, both of which are partly covered elsewhere.

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 73%, so most parameters are self-documenting. The description enumerates the group_by values (already in the enum) and adds a cross-reference to list_conditions for the condition slug, but several filters such as min_sample_size and the year range receive no extra meaning.

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 (count) and resource (studies) plus the eight available grouping facets, and frames the intent as "trends and 'how many' questions". This clearly separates it from retrieval-style siblings like search_studies and get_study.

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?

Gives a clear use context (trends, count questions) and points to list_conditions for resolving condition values. It does not name the alternative tools or state when not to use aggregation, so no explicit exclusion.

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