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

With no annotations, the description carries the full burden, and it does disclose two genuinely useful behaviors: grouping commas and currency symbols are handled, and non-numeric rows are excluded but counted. It is silent on permissions, error behavior for a fully non-numeric or unknown column, and the response shape, so the disclosure is real but incomplete.

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 sentence with the metric list front-loaded and the data-cleaning caveats tucked into a parenthetical. Efficient, though the parenthetical makes it slightly run-on.

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?

No output schema exists, but the description enumerates the returned metrics, which is what an agent needs. Combined with the one-parameter schema, it is nearly complete, missing only error/edge-case behavior for invalid or 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 one required parameter, 'column', with 0% schema description coverage, so the description must compensate. It implies the column must be numeric and implicitly identifies it by name, but adds no detail on name matching, case sensitivity, or behavior for a non-numeric column.

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 PropFirmPicker dataset) and enumerates the six statistics returned, so an agent knows precisely what this computes. It is clear and specific, though it never explicitly contrasts itself with siblings 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?

No when-to-use guidance and no alternatives are named. The agent must infer from the metric list that this is the tool for aggregate statistics on a numeric column, with no exclusions or routing advice versus the eight sibling dataset_* tools.

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