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

No annotations are provided, so the description carries full behavioral burden. It discloses some behaviors: grouping commas and currency are handled, non-numeric rows are excluded and counted, which is useful for understanding how raw data is processed. However, it does not mention whether the tool mutates data (it implies read-only but doesn't explicitly state it), or any errors that might occur, such as what happens if the column is absent or has no numeric values.

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 concise—a single sentence that lists the exact statistics and key data-handling behaviors. It front-loads the main purpose (summarizing a numeric column) and then provides processing details. It could be slightly more structured (e.g., separating the statistic list from the caveats), but it is effective and not verbose.

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?

The tool is relatively simple with one parameter and no output schema, so the description covers the main functionality. However, it lacks information about how to specify the column if the dataset has multiple tables or aliases, and it doesn't mention what the response structure looks like (though the output schema is absent, the agent might benefit from a brief note on the return format). Given the simplicity, the description is adequate but not fully comprehensive.

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%, so the description must compensate. It clarifies that the 'column' parameter refers to a numeric column and implies the column exists in the BreakerDesk dataset. It mentions that grouping commas and currency are handled, which hints at data formats the parameter may accept, adding meaning beyond the schema's minimal definition. While it doesn't enumerate possible values, for a single string parameter this is sufficient.

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 clearly states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the BreakerDesk dataset, which is a specific verb-resource combination. It distinguishes itself from sibling tools that likely handle other dataset operations, though it does not explicitly mention any sibling by name.

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 implies usage by stating the tool computes column statistics and mentions handling of grouping commas/currency and exclusion of non-numeric rows, giving context for when it applies. However, it does not explicitly state when to use this tool versus alternatives like dataset_search or dataset_compare, nor does it state exclusions. Since no alternative names are mentioned, guidance is less explicit than ideal.

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