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

No annotations are provided, so the description carries the full burden. It discloses important behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This is valuable edge-case information. It does not explicitly state read-only nature, but that is implied by the operation. It does not mention output format, but the listed statistics imply the return 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 a single, well-structured sentence. It front-loads the core statistics, then adds relevant edge-case handling. Every clause provides information, with 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?

The tool is simple with one parameter and no output schema. The description covers the main operation, lists the statistics returned, and mentions handling of special cases (commas, currency, non-numeric rows). It does not cover scenarios like missing columns or empty datasets, but for a simple stats tool this is adequate.

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%, so the description must compensate for the 'column' parameter. It mentions 'numeric column' but gives no guidance on how to specify the column (e.g., by name, index, or identifier). It also does not clarify that the column must exist or how errors are handled. The description adds minimal semantic value beyond the parameter name.

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. It is specific and unambiguous, though it does not explicitly contrast with sibling tools like dataset_row or dataset_search. The title reinforces the purpose.

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?

Usage is implied: use this when you need summary statistics for a column. It does not mention alternatives or when not to use it, nor does it specify prerequisites like column existence or data type requirements. The context of the Lugtorq dataset is given but no explicit routing to other tools.

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