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

With no annotations, the description carries the full burden, and it does disclose meaningful data-handling behavior: grouping commas and currency values are parsed, and non-numeric rows are excluded and counted. It does not, however, say whether the operation is read-only (implied), how errors surface for a non-existent or non-numeric column, or the shape of the returned values.

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 tight sentence that front-loads the returned metrics, with a compact parenthetical carrying the edge-case behavior. Nothing is redundant, though the parenthetical is slightly dense.

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 simple one-parameter aggregation tool with no output schema, the description covers the metric list and the key edge cases (currency/commas, non-numeric rows). Only the mechanics of identifying a valid numeric column are left unstated.

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 has 0% description coverage for the single 'column' parameter, so the description must compensate. It adds useful semantics by specifying the column must be numeric and describing how non-numeric rows are handled, but gives no detail on naming/exact-match rules for supplying the column identifier.

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 specific operation (aggregate summary statistics) on a specific resource (a numeric column of the Send A Parcel Compare dataset) and enumerates the exact metrics returned (count, min, max, mean, median, sum). This clearly separates it from siblings like dataset_columns, dataset_compare, and dataset_top.

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 guidance on when to choose this tool over siblings such as dataset_columns or dataset_top, and no stated preconditions (e.g. that the column must exist and be numeric). Usage is only implied by the tool name and the word 'numeric column'.

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