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

With no annotations, the description carries the behavioral disclosure burden. It does well by explaining that grouping commas and currency are handled and that non-numeric rows are excluded and counted. This adds real context beyond the schema, though it omits details about error cases or return shape.

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 entire description is a single sentence that lists the computed statistics and the key data-handling behaviors. Every clause earns its place, and the main purpose is front-loaded.

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 input selection, preprocessing behavior, and the metrics returned. It could be more explicit about the exact return format, but the listed statistics make the expected output fairly clear.

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%, so the description must compensate. It narrows the 'column' parameter to a numeric column and mentions preprocessing behaviors, which helps. However, it does not specify whether the value is a column name, label, or index, or how column names map to the dataset.

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 identifies the tool as computing count, min, max, mean, median, and sum for a numeric column of the Binstockly dataset. This is specific and actionable, though it does not explicitly contrast itself with sibling tools such as dataset_top or dataset_search.

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 reasonably implied: call this when summary statistics for a numeric column are needed. The description also clarifies how messy numeric input is handled. However, it does not state when not to use it or name alternatives among the siblings.

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