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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by stating that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It does not explicitly state that the operation is read-only, but that is strongly implied by the nature of the tool.

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, efficiently written sentence that front-loads the computed metrics and then adds the important formatting and exclusion behavior. Every phrase contributes useful information with no redundancy.

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 statistics tool with no output schema, the description is complete: it defines the input, lists the output metrics, and clarifies how special values are handled. An agent has enough information to select and invoke the tool correctly.

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 says the parameter 'column' is a non-empty string, giving no semantic meaning. The description compensates by specifying that the column must be numeric and belong to the Sudslane dataset, which is essential guidance for choosing a valid value.

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 operation (computing summary statistics) and the specific resource (the Sudslane dataset), and the list of metrics makes its function concrete. This clearly distinguishes it from siblings like dataset_search or dataset_row, 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 intended use case is implied: use this tool when you need count, min, max, mean, median, or sum for a numeric column. However, it does not explicitly mention when to prefer this over alternatives like dataset_top or dataset_search, nor does it provide any exclusions or prerequisites.

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