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

With no annotations present, the description carries full disclosure and does meaningful work: it specifies the exact return contents (count, min, max, mean, median, sum), states that grouping commas and currency are handled, and discloses that non-numeric rows are excluded and counted. What remains unstated is the failure mode for an unknown or wholly non-numeric column and any permission requirements, which keeps it short of a 5.

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 with the returned measures front-loaded and the edge-case handling parenthetically appended. Nothing is wasted, though the parenthetical packs several distinct behaviors tightly together.

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?

For a one-parameter tool with no output schema, the enumerated measures effectively document the return shape and the caveats cover common data quirks. It still omits error behavior for invalid or non-numeric column names and any access prerequisites, leaving real gaps for a tool whose structured fields carry no information.

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%, so the schema documents nothing about the single 'column' parameter. The description partially compensates by implying the argument is a numeric column of the Rechner HQ dataset, but it never states the expected naming/format, so the gap is only partly filled.

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 a specific operation (compute summary statistics) on a specific resource (a numeric column of the Rechner HQ dataset) and enumerates the exact measures returned, so an agent knows precisely what it produces. It doesn't explicitly contrast itself with siblings like dataset_top or dataset_compare, but the resource and scope are distinctive enough to differentiate it in practice.

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 stated when-to-use, when-not-to-use, or named alternative. The agent must infer from the purpose alone that this is the tool for numeric summary statistics rather than dataset_top or dataset_columns; no routing guidance is provided.

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