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

With no annotations, the description carries the full burden and does meaningful work: it discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded but counted. It omits error behavior (e.g. what happens for a non-numeric or nonexistent column) and confirms nothing about read-only safety, keeping it short of a 5.

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 dense sentence that front-loads the returned metrics and appends the caveats in a parenthetical. No filler, everything earns its place.

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 usefully enumerates the returned metrics, which is the key missing piece an agent would need. It leaves minor gaps around failure modes and result typing, but is otherwise sufficient for a one-parameter aggregation tool.

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 adds one real constraint (the column must be numeric) but never states expected identifier format, case sensitivity, or how the column is referenced, so compensation is only partial.

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 precise operation (count, min, max, mean, median, sum) on a precise resource (a numeric column of the Bank Code Lookup dataset), so the agent knows exactly what will be computed. It doesn't explicitly contrast itself with siblings like dataset_top or dataset_columns, but the statistics scope makes the distinction inferable.

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?

Usage is implied: it is the tool to call when summary statistics of a numeric column are needed, and the parenthetical restricts input to numeric columns. There is no explicit when-to-use/when-not guidance or named alternative for non-numeric columns (e.g. pointing to dataset_top or dataset_search).

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