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

With no annotations provided, the description carries the behavioral burden. It usefully discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. This gives meaningful insight into data-cleaning behavior beyond the basic 'compute stats' operation.

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 well-organized sentence that front-loads the operation, names the target dataset, and folds the important data-cleaning caveats into the same statement. There is no wasted text.

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 summary tool with no output schema, the description covers what the tool computes, what data it operates on, and how anomalies are handled. It does not spell out the exact output shape or error behavior, but this is a minor gap given the tool's simplicity.

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 'column' is a non-empty string, and description coverage is 0%. The description compensates by explaining that the column must be numeric and part of the DailyLogDesk dataset, and by describing how numeric formatting is normalized. It does not enumerate valid column names, but the single-parameter shape makes the meaning sufficiently clear.

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 (compute count, min, max, mean, median, sum) on a specific resource (a numeric column of the DailyLogDesk dataset). This clearly distinguishes it from sibling tools like dataset_row, dataset_search, or 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 description makes the intended use clear: summarizing a numeric column. However, it never explicitly says when to prefer this over siblings, nor does it mention that it is the wrong choice for non-numeric or row-level tasks. Usage context is implied rather than stated.

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