summary

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

With no annotations provided, the description must fully disclose behavioral traits. It does not mention whether the tool modifies data (likely read-only, but unstated), if it works on non-numeric columns or raises errors, or if there are limits on dataset size. The return format is not described, though an output schema exists; however, the schema is not shown and the description adds no behavioral context.

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 short and to the point, with a simple one-line summary and a single parameter in an Args block. However, the 'whole dataset' claim is ambiguous (summary of all columns? or just one column?). The structure is acceptable but the content is too sparse to be effective.

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 tool has 1 parameter, no annotations, no enum constraints, and an output schema (though not detailed). Given the simplicity, the description could be more complete. It doesn't explain what output to expect, how to interpret the results, or error handling. For a numeric analysis tool, this is insufficient for correct agent invocation.

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?

Schema description coverage is 0%, so the description must compensate for the schema's lack of parameter documentation. It only says 'name: the dataset name', which is minimal and adds no extra semantics—no format, no examples, no clarification of case sensitivity or allowed characters. A baseline of 3 is not merited because the description fails to add value.

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 the tool returns 'summary statistics' for a column or whole dataset, but 'summary statistics' is vague—it doesn't specify what statistics (e.g., mean, median, count, missing values). The sibling tools include load_csv, filter_rows, top_rows, and correlation, but the description fails to differentiate summary from correlation or top_rows, which also provide data insights.

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 given on when to use this tool versus siblings. For example, it doesn't clarify when to use summary versus correlation for numerical analysis, or top_rows for previewing data. There's no mention of prerequisites (e.g., dataset must be loaded via load_csv), or when this is preferred over other analytical tools.

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