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

With no annotations, the description carries the full burden and does add real behavioral detail: commas/currency are normalized, non-numeric rows are excluded and counted. It stops short of stating the operation is read-only, error behavior for a non-numeric column, or how the excluded count is surfaced.

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, with the handling caveats parenthetically appended. No wasted words, though the parenthetical packs three distinct behaviors together.

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, so the description correctly enumerates the return values (count, min, max, mean, median, sum). For a one-parameter read tool the coverage is nearly sufficient; only result structure/pagination and failure modes are unstated.

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 'column' parameter is undocumented in the schema (0% coverage), so the description must compensate. It does constrain the parameter semantically to a numeric column and describes how non-numeric values are handled, which is more than the bare schema offers, but it gives no naming/format guidance for the column identifier.

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?

Names the exact operation (count, min, max, mean, median, sum) and the exact target (a numeric column of the Excurvo dataset). An agent can distinguish this from dataset_columns or dataset_top by the returned metrics, though no sibling is named explicitly.

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 only implied by the stated output metrics; there is no explicit when-to-use statement and no mention of alternatives such as dataset_compare or dataset_top. The agent must infer that this is the tool for per-column aggregates.

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