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

With no annotations, the description carries the full behavioral burden. It usefully discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is real behavioral detail. However, it does not describe return format, precision, missing-column behavior, or whether the operation is read-only.

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?

The description is a single front-loaded sentence that lists the computed metrics first and then adds handling details. It is efficient and avoids filler, though the parenthetical detail slightly interrupts the flow.

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 statistics tool with no output schema and no annotations, the description covers the computed metrics and key data-handling behavior. It still omits return-value structure, precision, and empty/all-non-numeric edge-case behavior, leaving some gaps an agent may need to infer.

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 parameter is not named in the description. The text implies that the column must be numeric and belong to the named dataset, which adds some semantic constraint, but it gives no valid column names, naming conventions, or format examples to compensate for the undocumented schema.

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 exactly what the tool computes (count, min, max, mean, median, sum) for a numeric column of a specifically named dataset. This is a concrete verb+resource statement that an agent can easily distinguish from sibling tools like dataset_columns or dataset_top.

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?

The description says what the tool does but gives no guidance on when to choose it over alternatives such as dataset_compare or dataset_top. There are no stated prerequisites, exclusions, or contexts for use.

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