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

With no annotations, the description carries the full burden and meaningfully discloses edge-case behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. It does not detail behavior for missing/empty columns or the exact return structure, but the key calculation 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 front-loads the statistic list and packs the data-cleaning caveat into a parenthetical. Every phrase adds value 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?

For a one-parameter statistics tool, the description covers the input meaning and the result content via the listed statistics, while the sibling list helps an agent locate its niche. Without an output schema, a small additional note on return shape and all-non-numeric-column behavior would make it fully complete.

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 description coverage is 0%, so the description must compensate. It clarifies that the single 'column' parameter should be a numeric column and that formatted numbers are normalized, but it does not specify whether the column is identified by name, label, or index, or what qualifies as numeric.

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 the specific resource (Shortcodo dataset) and an explicit list of computed statistics (count, min, max, mean, median, sum), which clearly differentiates it from sibling tools like dataset_row or dataset_search. The lack of an imperative verb is mitigated by the concrete statistic list.

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 intended use is implied: when a numeric summary of a column is needed. However, there is no explicit guidance about when not to use this tool or which sibling tool to prefer instead, such as dataset_top for ranking or dataset_search for filtering.

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