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 does add real behavioral detail: grouping commas and currency symbols are normalized before parsing, and non-numeric rows are excluded and counted rather than erroring. It does not state what happens if the named column is entirely non-numeric or how the excluded count is surfaced, leaving a modest gap.

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 list of computed statistics, followed by the important parsing/exclusion caveats in a compact parenthetical. Dense but every clause carries information; 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?

With no annotations and no output schema, the description does the key work of enumerating the returned values and the data-handling semantics. What is missing is minor: behavior on an all-text or nonexistent column and how the excluded-row count is reported.

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 that the column must be numeric and belong to the Immigration Adviser Finder dataset, but gives no naming format or example, so the guidance is only partially compensating for the schema gap.

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 (a numeric column of the Immigration Adviser Finder dataset) and enumerates exactly which statistics are computed (count, min, max, mean, median, sum), so an agent knows precisely what the tool produces. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, so it stops short of a 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?

Applicability is implied by the 'numeric column' qualifier, which tells the agent this tool only makes sense for numeric fields, but there is no explicit when-to-use guidance and no routing to sibling tools for non-numeric summaries or ranked views.

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