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

With no annotations, the description carries the full burden and largely meets it: it discloses that grouping commas and currency symbols are parsed before aggregation, and that non-numeric rows are excluded rather than erroring. It does not say what happens if the named column does not exist or is entirely non-numeric, so it falls short of full coverage.

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 returned statistics with edge-case handling relegated to a parenthetical. Efficient, with nothing wasted, though the opening lowercase list reads more like a stub than a polished definition.

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 and no annotations, so the description must convey both behavior and return shape; it enumerates every returned statistic and the data-cleaning rules. The remaining gap is how an agent should obtain valid column names, which the sibling dataset_columns would supply.

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, but the name is self-explanatory and the description adds a real constraint the schema lacks: the column must be numeric. However, it gives no format guidance and no way to discover valid column identifiers.

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 statistic set (count, min, max, mean, median, sum) and the resource (a numeric column of the Capital Gains Tax HQ dataset), so the agent knows precisely what is computed. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, but the operation is unambiguous.

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 reach for this tool versus dataset_top, dataset_compare, or dataset_row, nor any prerequisite such as discovering valid column names via dataset_columns. Usage is only implied by the phrase 'numeric column'.

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