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 genuinely useful behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded from the aggregate but still counted. It stops short of stating the return format, whether the call is read-only, or how missing/empty columns behave.

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 sentence that front-loads the output list and appends the two important data-handling caveats in parentheses. No filler, though the leading stat enumeration delays the 'what it does' framing slightly.

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?

With no output schema, the description does explain what comes back by enumerating the returned statistics, and it flags the two data quirks that affect correctness. For a one-parameter read tool this is close to sufficient; only the behavior on an all-non-numeric column is unaddressed.

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 coverage is 0% for the single 'column' parameter, so the description must compensate. Saying the column must be numeric is the only real constraint it adds; it does not say whether the value is a header name, where valid names come from (dataset_columns), or how a non-existent column is handled.

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 exact resource (summary statistics of a numeric column) and enumerates the six stats returned (count, min, max, mean, median, sum), scoped to the Working Capital Quotes dataset. That is specific enough to separate it from dataset_top and dataset_compare, though it never explicitly contrasts with those siblings.

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?

There is no when-to-use or when-not-to-use guidance and no alternative tool is named. The phrase 'numeric column' implicitly constrains the input, but an agent gets no help deciding between this and dataset_top, dataset_compare, or dataset_search.

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