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 disclose non-obvious data handling: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. That is meaningful behavioral context beyond a restatement of the title. It stops short of describing response shape or behavior for an all-non-numeric column, keeping it out of 5 territory.

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 that leads with the returned metrics before the dataset-scoping parenthetical. Dense but no wasted clauses; the parenthetical on parsing/exclusion earns its place.

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 correctly enumerates the returned statistics, so the agent knows what comes back. The only gap is edge-case behavior (e.g., a column with zero numeric values) and how the excluded-row count is surfaced.

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% and the single 'column' property has only minLength, so the description must compensate. It partially does by implying the argument is a column of the Historia Pojazdu VIN dataset and must be numeric, but it gives no naming convention, case sensitivity, or example values.

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 states a specific verb+resource (compute summary statistics for a numeric column) and enumerates the exact outputs returned (count, min, max, mean, median, sum). It is clearly distinguishable from siblings like dataset_columns or dataset_row, though it never names an alternative to route against.

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 rather than stated: the mention of numeric columns and exclusion of non-numeric rows signals this tool is for aggregating numeric fields only. There is no explicit when-to-use guidance, no mention of alternatives for categorical columns, and no prerequisites.

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