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

With no annotations, the description carries the behavioral disclosure burden. It usefully reveals that grouping commas and currency are handled and that non-numeric rows are excluded from stats but counted, which is valuable edge-case behavior. It stops short of describing error cases or exact output structure, but those are secondary here.

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 compact sentence front-loads the output statistics and then appends the parsing/non-numeric caveats in parentheses. There is no filler or redundancy.

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?

Given one simple parameter, no annotations, and no output schema, the description covers purpose, the returned statistics, and important data-handling behavior. It does not specify the response format or edge cases like all rows being non-numeric, but the missing details are unlikely to prevent correct invocation.

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 defines 'column' as a non-empty string, while the description adds that it refers to a numeric column and clarifies how values are normalized. With a single required parameter, the mapping from description to the 'column' property is unambiguous.

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 exactly which statistics are produced (count, min, max, mean, median, sum) and the specific resource they apply to (a numeric column of the Threewayly dataset). This clearly distinguishes dataset_stats from sibling tools that return columns, rows, search results, or comparisons.

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 the intended use: call this when you need summary statistics for a numeric column. It does not explicitly name sibling tools or state when to prefer dataset_columns, dataset_top, or dataset_search instead, so the guidance is moderate rather than explicit.

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