Summary by group

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

The annotations already declare this as a non-destructive, read-only, closed-world operation, so the description need not restate safety. It adds useful behavioral context: results are descriptive only, no p-values are produced, omitting value_column switches to row counts, and confirmation is required before execution.

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 front-loaded with the core operation and uses short follow-up sentences for key constraints. The first sentence is dense with examples and statistic names, but those examples earn their place by clarifying the tool's scope.

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?

The definition is adequate for the core grouped-summary use case, but for an 8-parameter tool with no output schema it omits some parameter-level details and does not describe the return shape or top-N behavior beyond a brief mention. The schema covers some gaps, such as file input and confirmation_token, but not all.

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?

With schema description coverage at 38%, the description does add meaning for the core parameters: group_column is the category, value_column is the numeric column, stats are the summary measures, and sorting/top N are mentioned. However, it does not clarify sort_by, descending, top limits/defaults, or file handling, leaving several parameters dependent on the schema.

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 gives a clear verb-like operation and resource: grouped summary statistics over a numeric column by another category column. It distinguishes the tool from inferential siblings by stating 'Descriptive only, no p-values,' though it does not explicitly name alternatives such as describe or run_test.

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 examples make the intended use cases concrete, and the 'Descriptive only, no p-values' line excludes inferential use cases. It also notes the workflow prerequisite that the user confirms the columns first, but it does not explicitly route the agent to named alternative tools.

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