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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. It stops short of describing output format or edge cases like an all-non-numeric column, but the core behavior is transparent.

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 compact sentence that leads with the exact set of statistics and tucks edge-case handling into a parenthetical. Every clause earns its place with no 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?

The tool is simple, and the description enumerates the computed statistics, effectively indicating the expected result set. It also covers important data-cleaning behavior. The only notable omission is an explicit statement of the return shape, but this is easily inferable for a single-column summary tool.

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, with 0% description coverage. The tool description adds meaningful semantics by clarifying that the column must be numeric and that non-numeric values are excluded. For a single-parameter tool, this adequately compensates for the schema gap.

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 names a specific operation: computing six named statistics (count, min, max, mean, median, sum) for a numeric column of the CoilDesk dataset. This clearly differentiates it from siblings like dataset_columns, dataset_search, and dataset_top, which serve different purposes.

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: this tool is for getting aggregate numeric summaries of a column. However, it does not explicitly state when to choose this over sibling tools, nor does it provide exclusion criteria or alternative recommendations.

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