calculate_statistics

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds no additional behavioral context (e.g., side effects, rate limits, or special conditions), so it meets the baseline without contradiction.

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 concise, with the core purpose front-loaded and the parameter clarification following naturally. No unnecessary words or redundant details beyond what aids understanding.

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?

Given that an output schema is indicated as present, the description need not explain return values. The operation list and data type clarification provide sufficient context for a straightforward statistical tool, though edge cases (e.g., empty data) are not covered.

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 schema lacks descriptions (0% coverage), but the description compensates by listing the allowed operation values ('mean', 'median', 'mode', 'variance', 'stdev', 'summary') and clarifying 'data' as a list of numerical observations. This meaningfully helps the agent understand valid inputs.

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 action ('Calculate') and the resource ('statistical summaries') for a dataset of numbers. It is distinct from sibling tools like arithmetic or unit conversion, making the purpose unambiguous.

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?

No explicit guidance on when to use this tool versus alternatives. It implies statistical use but does not mention when not to use it or reference sibling tools like evaluation or arithmetic, leaving the selection decision to the agent.

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