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

Annotations already establish read-only/idempotent safety. The description adds domain-specific behavior: data is population-level only, unavailable cohorts are not extrapolated, scans are not interpreted, and pathology is not excluded. It does not cover auth or rate limits, but adds substantial context beyond annotations.

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?

Two compact sentences, front-loaded with the action and constraint, with no redundant restatement of the name or annotations.

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 two-parameter reference lookup with an output schema and explicit safety annotations, the description covers scope and interpretation limits adequately. The main omission is body_part context and sibling routing, but the schema and output schema carry the rest.

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%; the description only alludes to an 'age cohort' and never mentions the body_part parameter or its enum values. With two required parameters, the description does not compensate for the schema's lack of parameter descriptions.

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 return: population-level prevalence data, with a cohort-availability condition. This is clear enough to distinguish from generic evidence/timeline siblings, though it does not explicitly name those alternatives.

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 precondition ('only when the dataset contains the requested age cohort') and explicit non-uses (no extrapolation, no individual scan interpretation, no pathology exclusion). It stops short of naming sibling tools to use for individual interpretation or evidence lookup, so routing is incomplete.

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