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

With no annotations, the description carries the full behavioral burden, and it does disclose non-trivial data-handling semantics: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. However, it says nothing about error behavior (e.g., a fully non-numeric or missing column), rounding of results, or any safety/permission profile.

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?

One dense sentence that front-loads the returned metrics and uses a parenthetical for the edge-case handling. Nothing is wasted, though the metric list is long enough that scanning is slightly harder than a bulleted form would be.

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 and the exclusion/counting behavior, which is what an agent needs to interpret results. It would be complete with a note on how non-numeric-only columns are handled, but for a single-parameter statistics tool this is nearly sufficient.

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?

There is a single required parameter with 0% schema description coverage, so the description must compensate. It adds that the target must be a numeric column, which is a real constraint, but it never explains the identifier format (column name vs index), case sensitivity, or how to discover valid 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?

States a specific computation (count, min, max, mean, median, sum) over a specific resource (a numeric column of the Cookievo dataset), so the agent knows exactly what will be returned. It does not explicitly differentiate itself from siblings like dataset_top or dataset_compare, which is the only thing keeping it out of the top band.

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 is purely functional and gives no when-to-use guidance, prerequisites, or named alternatives. It never says how this differs from dataset_top or dataset_compare, so the agent must infer selection from the tool name alone.

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