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

With no annotations, the description carries the full burden and does meaningful work: grouping commas and currency symbols are handled, and non-numeric rows are silently excluded but counted. It does not state what happens when the named column does not exist or is entirely non-numeric, so error behavior remains unclear.

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 metrics before the parsing caveats in parentheses. Nothing is wasted, though the parenthetical is crammed rather than broken out.

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 usefully enumerates the returned fields, which is the key missing structured information. With no annotations and one simple parameter, the remaining gaps (error cases, column naming convention) are minor.

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% with one required string parameter, so the description must compensate. It adds the constraint that the column must be numeric and belong to the VPNCompareHQ dataset, but gives no format, casing, or example value for the column argument.

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 the exact metrics returned (count, min, max, mean, median, sum) and scopes them to a numeric column of the VPNCompareHQ dataset, which an agent can distinguish from dataset_top or dataset_compare. It stops short of explicitly contrasting itself with any sibling tool.

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 metrics listed — an agent can infer this is for summarizing one numeric column — but the description never states when to choose this over dataset_top, dataset_compare, or dataset_search, nor any prerequisite (e.g., the column must be numeric).

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