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

With no annotations, the description carries the behavioral burden, and it does disclose real data-handling behavior: grouping commas and currency are handled and non-numeric rows are excluded and counted. That tells the agent how dirty input is treated, which is more than a bare 'compute statistics' statement. It still omits edge cases (empty or all-non-numeric column) and permission/error behavior.

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 front-loaded sentence that leads with the exact statistics returned and packs the parsing caveats into a trailing parenthetical. Dense but every clause earns its place; no filler.

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 read tool with no output schema and no annotations, the description supplies both the return contents (the six statistics) and the input-cleaning rules. The remaining gap is behavior on degenerate input and how to discover valid column names, which are minor against the tool's simplicity.

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 coverage is 0% and the schema only says 'column: string, minLength 1'. The description compensates partially by constraining the argument to a numeric column of a specific dataset, but it does not explain naming conventions or how a caller finds a valid column name (e.g., via dataset_columns).

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?

States a specific verb+resource: it computes a named set of summary statistics (count, min, max, mean, median, sum) for a numeric column of the Med Spa Cost Checker dataset. The scope is precise enough that an agent can distinguish it from dataset_top or dataset_compare, though no sibling is named explicitly.

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?

The phrase 'of a numeric column' implies the precondition for use (the column must be numeric) and the dataset scope, which is a real usage constraint. However, it never says when to prefer this over dataset_top, dataset_compare, or dataset_search, so the alternative-selection guidance is left implicit.

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