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

No annotations are provided, so the description carries the full behavioral burden. It does disclose that grouping commas and currency are handled and that non-numeric rows are excluded and counted—useful behavioral context. However, it does not describe the output structure, error handling, or whether the column must already exist.

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?

The description is a single sentence that front-loads the statistic list and then places the row-handling caveats in parentheses. It is economical with words, though the dataset name 'Cmmsly' appears to be misspelled.

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, the description adequately covers the target column and preprocessing behavior. But with no output schema and no annotations, an agent is left without information about the return format (e.g., dictionary vs list) and potential error conditions, leaving some ambiguity.

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 only parameter is 'column' defined as a non-empty string. The description adds meaning by clarifying the column must be numeric, but it does not specify how the column name is passed or how edge cases like whitespace or special characters are handled. Partial compensation for the schema gap.

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 explicitly enumerates the computed statistics (count, min, max, mean, median, sum) and identifies the target resource as a numeric column of the Cmmsly dataset. This distinguishes it from siblings like dataset_columns or dataset_top, though it relies on the title 'Summary statistics' to supply the verb.

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 provides no guidance on when to use dataset_stats versus alternatives such as dataset_search or dataset_top. It implies that the tool is for numeric column summaries, but it does not state selection criteria or exclusions.

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