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

With no annotations, the description carries the full disclosure burden. It does add genuinely useful behavioral context — grouping commas and currency are normalized, and non-numeric rows are excluded but still counted — which is more than most definitions offer. It stops short of describing missing-value handling, error behavior for non-numeric columns, or return shape.

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 returned metrics followed by the data-handling caveats. The parenthetical is dense but every clause carries information, and there is no filler or restatement of the tool name.

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 description effectively substitutes by enumerating the returned statistics, and it flags the numeric-only constraint and exclusion behavior. The main remaining gap is that an agent gets no hint about valid column identifiers or what happens when no numeric rows exist.

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 single parameter 'column' has 0% schema description coverage, so the description must compensate. Saying the tool operates on 'a numeric column' implies the argument is a column name and is expected to hold numeric data, which adds real meaning beyond the bare string type, though it does not say how columns are referenced or whether names are case-sensitive.

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 resource (a numeric column of the Tide Times Compare dataset) and enumerates the specific outputs (count, min, max, mean, median, sum), so the tool's function is unambiguous. It does not, however, distinguish itself from siblings like dataset_top or dataset_compare, leaving routing to inference.

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 when-to-use, when-not-to-use, or alternative-tool guidance. The only implicit signal is that the column must be numeric, which an agent could infer from the outputs listed, but nothing tells the agent when this beats dataset_top or dataset_row.

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