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

No annotations exist, so the description carries the full behavioral burden. It does disclose useful data-handling behavior (grouping commas and currency parsed, non-numeric rows excluded and counted), but says nothing about permissions, error behavior for a non-numeric or nonexistent column, or whether aggregation is scoped to the whole dataset.

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 that front-loads the returned statistics and parenthetically carries the qualification rules. Nothing is wasted.

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 read tool with no output schema, enumerating the exact statistics returned and the row-exclusion behavior makes it nearly callable as-is. Only error/target-scope behavior is missing.

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% with one required parameter, so the description must compensate. It adds one meaningful constraint (the column must be numeric) but gives no hint about column naming conventions or accepted identifiers.

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?

States a specific action (compute count/min/max/mean/median/sum) on a specific resource (a numeric column of the RMMCompare dataset). The enumerated statistics make the tool's function unambiguous, distinguishing it implicitly from siblings like dataset_compare or dataset_row, though no sibling is named.

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?

There is no guidance on when to use this versus dataset_compare, dataset_top, or dataset_columns. Usage must be inferred purely from the fact that it summarizes a numeric column.

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