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

With no annotations, the description carries the full burden and does disclose non-obvious parsing behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it does not clarify the return format, and the phrase 'excluded and counted' is ambiguous as to whether the reported count represents numeric rows or excluded rows.

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?

The single sentence front-loads the result list, scopes the resource, and appends parsing details in a parenthetical. Every clause carries distinct information with no redundancy or wasted words.

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, the description covers purpose, parameter semantics, and key behavioral quirks. It stops short of specifying the exact return shape or edge-case behavior, but this is a minor gap given the tool's low complexity.

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 schema only defines 'column' as a string with no description. The tool description adds meaning by specifying that the column must be numeric and by describing how values are processed (commas/currency handled, non-numeric excluded), which helps an agent select appropriate input.

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 statistics produced (count, min, max, mean, median, sum) and the target resource (a numeric column of the Opexvo dataset), which clearly distinguishes it from siblings like dataset_row or dataset_search. The title reinforces this purpose.

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 description implies use when summary statistics of a numeric column are needed, but it does not explicitly state when to prefer this tool over siblings such as dataset_top or dataset_search, nor any exclusions. Usage guidance is only implicit.

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