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 add real behavioral detail: grouping commas and currency are handled, non-numeric rows are excluded and counted. However, it omits error behavior (what happens for a non-numeric or unknown column), output shape, and any performance/read-only framing.

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 dense sentence that front-loads the returned statistics and tucks data-handling caveats into a parenthetical. No filler, though the enumeration makes it slightly heavy to parse.

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?

There is no output schema, but the description effectively enumerates the returned fields (count, min, max, mean, median, sum), so an agent knows what comes back. Remaining gaps — empty-result and error handling for non-numeric columns — are minor for a simple read-only aggregation tool.

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?

Schema coverage is 0%, so the description must compensate. It does convey that the single 'column' argument names a column and that it must be numeric, which is meaningfully more than the bare string type in the schema, though it does not clarify matching rules (case sensitivity, exact name vs. alias).

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 operation (compute summary statistics) on a specific resource (a numeric column of the Buffrota dataset) and enumerates the exact outputs. It is clearly distinguishable from row/list siblings like dataset_row or dataset_search, though it never names or contrasts a sibling 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: an agent can infer it is for aggregating one numeric column, but there is no statement of when to prefer it over dataset_compare, dataset_top, or dataset_columns, 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.