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

With no annotations, the description carries the full burden, and it does disclose non-trivial data-handling behavior: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. It does not state permissions, read-only nature, or what the count field represents relative to the excluded rows, but for a read-only aggregate query the disclosed edge-case handling is solid.

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 dense sentence that front-loads the returned statistics, then packs the data-cleaning caveats into a compact parenthetical. Nearly every clause earns its place; the parenthetical is information-dense rather than padded.

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?

There is no output schema, so the description correctly enumerates the return values (count, min, max, mean, median, sum), which is the main thing an agent needs. The remaining gap is a lack of guidance on dataset selection or failure behavior when the column is entirely non-numeric.

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% and the single 'column' parameter has no description in the schema, but the prose constrains it usefully by requiring a numeric column and by noting non-numeric input rows are excluded rather than rejected. That adds meaning beyond the bare string/minLength schema, but format or naming conventions for the column are still unstated.

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?

Names a specific operation (summary statistics) on a specific resource (a numeric column of the Medicare Plan Comparison dataset) and enumerates the outputs. An agent can tell it apart from dataset_row, dataset_top, and dataset_search by the aggregate-statistics framing, though no sibling is named explicitly.

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 conveys implied usage (get stats for a numeric column) but gives no when-to-use guidance, no exclusions, and no routing to alternatives such as dataset_top or dataset_row. It also does not say what to do if the requested column is non-numeric.

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