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

With no annotations, the description still discloses key behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. That tells the agent how data is cleaned and that exclusions are reported, though it omits error handling and read-only nature (which is implied for stats).

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 lists all returned metrics first and then adds two important data-handling clauses. No filler, front-loaded, entirely earned.

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 simple one-parameter statistical tool with no output schema or annotations, the description provides the output metrics and key data-cleaning rules. It leaves minor gaps around error behavior and exact column addressing, but the core invocation context is present.

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 input schema has 0% description coverage for the single `column` parameter. The description compensates by specifying that the column must be numeric and part of the Lettza dataset, but it doesn't explain naming format, case sensitivity, or how to handle non-existent columns.

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 exact statistics returned (count, min, max, mean, median, sum) and the resource (a numeric column of the Lettza dataset), so the agent knows precisely what the tool computes. It does not explicitly differentiate from sibling tools like dataset_columns or dataset_top, but the specificity of the metrics makes its role clear.

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 statement of when to use this tool versus the sibling dataset_* tools, nor any prerequisites or exclusions. The description is purely functional and offers no routing guidance.

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