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 data-handling behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. However, it says nothing about permission requirements, failure modes (e.g. a column with zero numeric values), or the shape of the returned result.

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 efficient sentence with the returned metrics front-loaded. The parenthetical caveat about commas, currency, and excluded rows is dense but 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?

For a one-parameter tool with no output schema and no annotations, the description is nearly self-sufficient: it enumerates the exact statistics returned and the edge-case handling. Only the column-name source and error behavior 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 single 'column' parameter has 0% schema description coverage, so the description must compensate. It does convey that the column must be numeric and that it belongs to the named dataset, but it does not explain valid column naming or point to dataset_columns for discovery.

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?

Names a specific operation (count, min, max, mean, median, sum) on a specific resource (a numeric column of the Crypto Exchange Compare HQ dataset). An agent can distinguish this from siblings like dataset_top, dataset_compare, and dataset_row, 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 description never says when to reach for this tool versus dataset_top, dataset_compare, or dataset_search, nor does it state prerequisites such as needing a valid column name. Usage is only weakly implied by the word 'stats'.

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