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

With no annotations, the description carries the full behavioral burden, and it does disclose non-obvious traits: comma/currency grouping is normalized and non-numeric rows are both excluded and counted. It stops short of covering failure behavior (e.g., invalid or missing column) or confirming read-only semantics.

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 front-loaded sentence listing the returned metrics, with the parsing/exclusion caveats compacted into a parenthetical. Nothing is wasted, though the parenthetical is dense enough to bury two distinct behaviors.

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 exact return values, and it explains the data-cleaning edge cases. Remaining gaps are error handling and column reference format, which are minor for a one-parameter aggregate 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?

The single 'column' parameter has 0% schema description coverage, so the description must compensate. It adds real meaning — the column must be numeric and must belong to the FlightDelayHQ dataset — but provides no naming format, examples, or case-sensitivity guidance.

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 enumerates exactly what is computed (count, min, max, mean, median, sum) over a numeric column of a named dataset, so an agent immediately knows the operation and scope. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, so it falls short of 5.

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 only implied: the phrase 'numeric column' signals the tool is inapplicable to text columns, but there is no explicit when-to-use statement and no alternatives (e.g., dataset_top for ranked values, dataset_compare for cross-group comparison) are named. An agent must infer routing from the sibling list alone.

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