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

With no annotations, the description carries the full behavioral burden. It discloses two non-obvious behaviors: grouping commas/currency are parsed, and non-numeric rows are excluded and counted. The phrase 'excluded and counted' is slightly ambiguous, and return format is not described, but the disclosed edge-case handling is strong.

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 compact, front-loaded sentence: the computed statistics appear first, with parsing edge cases in a parenthetical. There is no filler or redundant restating 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 simple one-parameter statistics tool, the description covers the target column, the computed values, and important parsing behavior. The main gap is that no output schema exists and the description does not explicitly describe the return shape, but the enumerating of output statistics partially compensates.

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% and the schema only says 'column' is a string. The description adds that the column must be numeric and that formatted values are handled, which helps, but it does not specify whether the value should be an exact column name, display label, or key, nor how ambiguity is resolved.

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 states exactly what the tool does: it computes count, min, max, mean, median, and sum for a numeric column of the Ppmly dataset. This enumerated output list clearly differentiates it from search, compare, provenance, and top-value sibling tools.

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?

The usage context is implied: call this when aggregate numeric statistics for a column are needed, and the description notes handling of formatted numbers. However, it never explicitly contrasts this with sibling tools or states when not to use it, leaving routing to inference.

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