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 meaningful data-handling behavior: grouping commas and currency symbols are normalized, and non-numeric rows are excluded and reported as a count. It stops short of describing failure behavior (e.g., a fully non-numeric or misspelled column) or the response 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?

A single dense sentence that front-loads the returned metrics and follows with scope and handling rules, so scanning it answers 'what do I get' immediately. The parenthetical adds real information rather than padding, though the sentence is tightly packed.

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, enumerating the returned statistics in the description is exactly what is needed. The remaining gap is the undocumented format of the 'column' argument, which matters given 0% schema coverage.

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 has 0% schema description coverage, so the description must compensate. It adds only an implied constraint (the column must be numeric, and belongs to the Asbestos Survey Cost dataset) but never states the expected format, such as a header name or case sensitivity.

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 resource and enumerates exactly what is computed (count, min, max, mean, median, sum) on a numeric column of a named dataset, which is far more concrete than a generic 'stats' label. It does not, however, explicitly differentiate itself from siblings like dataset_compare or dataset_top, so the agent must infer the boundary.

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 by the tool name and the listed outputs: it is the tool for descriptive statistics on one numeric column. There is no statement of when to prefer it over dataset_top, dataset_compare, or dataset_search, and no prerequisites or exclusions are given.

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