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

With no annotations, the description carries the full behavioral burden and it handles two non-obvious behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not describe return format or error cases, but the key data-cleaning caveats are disclosed.

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 dense sentence that front-loads the computed statistics and appends the important data-handling caveats in a parenthetical. There is no filler or repetition.

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 is largely complete: it defines the primary behavior and the important edge-case handling. It would benefit from specifying the output shape or behavior when all rows are non-numeric, but the core usage is clear.

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 schema only says the parameter is a non-empty string, so the description adds important meaning: the column must be numeric and values may include grouped commas or currency symbols. This goes beyond the minimal schema information.

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 states exactly what the tool computes—count, min, max, mean, median, and sum—for a numeric column of a specific dataset, which clearly distinguishes it from sibling tools like dataset_row, dataset_search, and dataset_top.

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 intended use is implied: use this tool when summary statistics for a numeric column are needed. However, it never names sibling tools or states when not to use it, leaving the selection decision partially to inference.

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