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

With no annotations provided, the description carries the full burden, and it does disclose real behavior: grouping commas and currency formatting are handled, and non-numeric rows are excluded and counted. That is meaningful data-cleaning context an agent could not guess. It still omits error behavior for an invalid or non-existent column name.

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?

A single sentence that front-loads the six statistics before qualifying details in a compact parenthetical. Nothing is wasted, though the parenthetical packs three distinct behaviors into one clause.

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?

For a one-parameter tool with no output schema, the description usefully enumerates the returned statistics and states the dataset scope, so an agent knows what it gets back. It falls short only on failure modes (bad column name) and on how to discover valid column names.

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% for the single 'column' parameter, so the description must compensate. It clarifies that the column must be numeric and belong to the Wen Receipts dataset, which adds genuine meaning, but gives no naming convention, examples, or pointer to dataset_columns for discovering valid values.

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 the exact resource (a numeric column of the Wen Receipts dataset) and enumerates the six statistics computed (count, min, max, mean, median, sum), so an agent knows precisely what it returns. It does not explicitly contrast itself with siblings like dataset_top, dataset_row, or dataset_compare, which keeps it from a 5.

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?

There is no statement of when to use this tool versus alternatives, nor any prerequisites or exclusions. Usage is only inferable from the fact that it operates on a single numeric column; nothing routes the agent between this and sibling dataset_* tools.

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