statistics_summary

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only restates the basic function. It does not specify which descriptive statistics are computed, how edge cases (e.g., empty arrays) are handled, or what the return structure looks like.

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 a single, clear sentence with no wasted words. It front-loads the main action and resource, making it instantly understandable and appropriately sized for a simple tool.

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 the tool has only one parameter fully described by the schema and an output schema exists, the description is mostly adequate. However, it leaves the exact set of statistics ambiguous, which could matter in selecting the right tool, though not critically for a simple summary tool.

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 input schema already fully describes the only parameter 'data' as a list of numeric values (100% coverage). The description adds the synonym 'dataset' but no additional constraints, examples, or clarifying details, so it remains at baseline.

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 states 'Compute descriptive statistics for a dataset' with a specific verb and resource, clearly distinguishing it from siblings like matrix_operation, solve_ode, and calculate_expression. The purpose is unambiguous and names a distinct domain.

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 guidance is provided on when to use this tool versus alternatives. There is no mention of conditions, exclusions, or how it relates to other statistics or math tools, leaving the agent without decision support.

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