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

No annotations are provided, so the description carries the full burden, and it delivers real behavioral detail: commas/currency are parsed, non-numeric rows are excluded and counted, and the exact set of returned statistics is enumerated. Missing only secondary traits such as behavior on large datasets or permission requirements (likely irrelevant for a read-only stat tool).

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 sentence, front-loaded with the returned statistics and followed by the parsing/exclusion caveats. No filler, though the parenthetical runs on slightly and could be split for faster scanning.

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 usefully enumerates the return values and edge-case handling, which is what an agent needs to interpret results. It falls short only in not pointing to dataset_columns for discovering valid numeric column names, which would close the last gap for a 1-param 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?

Schema description coverage is 0% for the single parameter, so the description must compensate. It adds meaning by constraining the argument to a 'numeric column' of a named dataset, but it does not specify the expected column-name format, case sensitivity, or what happens when a non-numeric column is passed.

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 Insurance by Profession dataset), so the agent knows exactly what it returns. It does not explicitly differentiate itself from siblings like dataset_top or dataset_compare, but the verb+resource pairing is unambiguous enough.

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 by the phrase 'numeric column' and by the stat list, so an agent can infer this is the tool for aggregate summaries. However, there is no explicit when-to-use/when-not guidance and no mention of alternatives (e.g. dataset_top for ranked values), leaving routing to inference.

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