get_patient_statistics

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

No annotations are provided, so the description carries the burden. It discloses the scope ('entire patient dataset') and output contents, adding some behavioral context. Yet it omits potential performance implications, data freshness, or any side effects, leaving partial transparency.

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 description is extremely concise: two sentences of purpose and contents, plus a simple return note. Every sentence earns its place with no redundancy, making it easy to scan.

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-parameter, read-only statistics tool with no output schema, the description covers the essential what and includes the main contents. It could add detail about formatting or calculation specifics, but the absence is not critical given the tool's simplicity.

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 has zero parameters, and the schema is empty (100% coverage). The description correctly avoids inventing parameter details, and the baseline for 0 params is 4, as there is nothing to explain.

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 clearly states the tool's function: 'Get summary statistics for the entire patient dataset' with specific contents (count, age, gender, disease distribution). This distinguishes it from sibling tools that focus on individual records or filtered lists.

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?

The description clearly implies use for dataset-level overview, contrasting with sibling tools that retrieve specific patients or diseases. However, it does not explicitly mention alternatives or exclusions, though the context from sibling names is helpful.

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