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

With no annotations provided, the description carries the full responsibility for behavioral disclosure. It usefully notes that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which goes beyond the schema. It does not mention error behavior or what happens if the column is missing, but the disclosed behaviors are meaningful.

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 sentence that front-loads the list of statistics, names the dataset, and appends important caveats in a parenthetical. Every part earns its place with no redundancy or unnecessary verbosity.

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 low-complexity, one-parameter tool with no annotations and no output schema, the description is fairly complete: it identifies the dataset, the column type, the returned statistics, and data-cleaning behavior. It lacks an explicit output shape or error semantics, but these are not critical for such a simple tool and can be inferred.

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 minLength 1 and no description, so schema description coverage is 0%. The description compensates by specifying that the column must be numeric and that formatted numbers (commas/currency) are handled. For a single parameter, this adds sufficient semantics beyond the bare schema.

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 explicitly states the operation (computing count, min, max, mean, median, and sum) and the resource (a numeric column of the BurdenRateLedger dataset). This clearly differentiates it from sibling tools like dataset_row or dataset_search, which serve different purposes.

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 use case is implied by the title and description: use when summary statistics of a numeric column are needed. However, the description does not explicitly contrast this tool with sibling alternatives or state when not to use it, leaving the guidance implicit rather than explicit.

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