describe

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

With no annotations provided, the description carries the full burden. It transparently lists what statistics are returned and how categorical columns are summarized, but it does not state side effects, missing-value handling, output shape, or behavior across groups. It is informative but leaves some behavioral uncertainties.

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 compact and front-loaded with the purpose, then enumerates the statistics in one dense sentence. The categorical sentence adds necessary behavior without fluff. It is slightly list-heavy but earns its space.

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?

There is no output schema, so the description reasonably carries the burden of explaining return values: it lists the statistics and categorical summary behavior. It is adequate for a 7-parameter analysis tool, though the exact output structure and grouped result layout are left unspecified.

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 already documents all parameters well, so the baseline is 3. The description adds value by clarifying that group_by accepts one or more grouping variables and that categorical columns yield a level count and modal category, which goes beyond the schema's generic 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?

The description names a clear resource and outcome: descriptive statistics for the session dataset, with a detailed list of computed measures and categorical fallback behavior. It differentiates from sibling tools like frequency_table and data_inspect by showing it returns aggregate summaries, not raw inspection or frequency tables.

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 implies when to use it—when summary statistics are needed—and adds context via grouping by variables. However, it does not explicitly say when not to use it or mention alternatives such as frequency_table for categorical distributions or data_inspect for data overview.

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