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 real behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. However, it omits what happens if the column contains no numeric rows, whether errors are raised for wholly non-numeric columns, and nothing about output ordering or response shape.

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?

One dense sentence, front-loaded with the returned metrics and followed by a parenthetical for edge-case handling. Every clause earns its place; only the absence of a leading verb phrase ('Compute…') keeps it from being ideal.

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 single-parameter tool with no output schema, the description usefully enumerates the return values and the data-cleaning rules, which is the key information an agent needs. The remaining gap is failure behavior for non-numeric or empty columns.

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% — the single 'column' parameter has only a minLength constraint and no description. The description compensates partially by specifying that the column must be numeric and belong to the EntitySearch HQ dataset, which is meaningful guidance, but it does not explain accepted column-name forms or matching rules.

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 enumerates the exact operation — computing count, min, max, mean, median and sum for one numeric column of the EntitySearch HQ dataset. This clearly separates it from siblings like dataset_columns or dataset_row. It never explicitly names an alternative tool, so it falls short of full sibling differentiation.

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?

No when-to-use or when-not-to-use guidance is given. An agent must infer that this is for single-column numeric summarization versus dataset_compare (multi-column comparison) or dataset_top (leaderboards) from the wording alone. The constraint that the column must be numeric is stated, but no routing advice accompanies it.

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