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

With no annotations, the description carries the full burden of behavioral disclosure and does a good job by explaining that grouping commas and currency are handled and that non-numeric rows are excluded and counted. This provides meaningful edge-case behavior beyond the basic stat computation. It could go further by specifying behavior for an empty dataset or a missing column, but the disclosed handling is valuable.

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 description is a single focused sentence that front-loads the computed statistics and then adds edge-case behavior. Every part contributes useful information with no redundancy or filler.

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 single-parameter tool, the description covers what is computed and how problematic values are handled. Since there is no output schema, explicitly listing the six output statistics helps fill that gap. It leaves minor unknowns like exact response structure and error handling, but overall the tool is adequately described for an agent to invoke it.

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 input schema only defines 'column' as a non-empty string, so the description adds important context by stating it is a numeric column of the FindAgency HQ dataset and that formatted numbers are supported. However, it does not specify the expected column identifier format, such as exact header name or case sensitivity, leaving some ambiguity for a parameter with 0% schema description coverage.

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 clearly states the operation: computing count, min, max, mean, median, and sum for a numeric column. This specific scope distinguishes it from sibling tools like dataset_row, dataset_search, and dataset_top, which serve different purposes. The title reinforces the resource and operation clearly.

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 intended use is implied by the description: call this tool when summary statistics for a numeric column are needed. However, there is no explicit guidance about when not to use it or which sibling might be preferred for other needs, such as retrieving raw rows or comparing datasets.

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