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 parsed and that non-numeric rows are excluded and counted, which is real value. It does not say what happens on a nonexistent column, an all-non-numeric column, or what form errors take.

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 sentence, front-loaded with the output metrics, with the dataset scope and parsing caveats tucked into a parenthetical. Dense and largely waste-free; the parenthetical is long but each clause adds information.

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, enumerating the returned metrics in the description is exactly the right compensation, and the parsing/exclusion notes cover the main data-quality surprises. Missing only error/edge-case behavior for invalid or fully non-numeric columns.

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 parameter at 0% schema description coverage, so the schema gives only type and minLength. The description indirectly constrains it (must be a numeric column, currency/grouping tolerated) but never defines the column identifier format, casing, or header naming.

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 operation (summary statistics), enumerates the returned metrics (count, min, max, mean, median, sum), and scopes it to a numeric column of a named dataset. That is specific enough to separate it from siblings like dataset_columns or dataset_row, though it does not explicitly name the sibling it is not.

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 agent can infer this is for numeric-column aggregation, and the note that non-numeric rows are excluded implies the column should be numeric. There is no explicit when-to-use/when-not guidance or routing to alternatives such as dataset_top or dataset_compare.

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