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

Since annotations are absent, the description carries the burden of disclosing behavior. It transparently states that non-numeric rows are excluded and counted, and that grouping commas and currency are handled. This provides useful insight beyond the basic function.

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, compact sentence that packs all necessary information without fluff. It is front-loaded with the main purpose and then adds relevant caveats.

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?

The tool is simple with one parameter and no output schema. The description explains what the tool does and the data quirks it handles. It does not explicitly state the return format, but that is not required for this level of complexity, making the description complete enough.

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 a string parameter with minLength, providing no semantic meaning. The description compensates by specifying that the column must be numeric, which is essential context. It could go further by explaining allowed formats or examples, but it adds clear 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 clearly states that the tool computes count, min, max, mean, median, and sum for a numeric column of a specific dataset. This is a precise verb and resource, and it distinguishes the tool from siblings like dataset_row or dataset_top.

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 provides context about when to use the tool: for summary statistics on a numeric column. It also notes handling of commas and currency and that non-numeric rows are excluded, giving the user a clear sense of applicable situations. It does not explicitly mention alternatives, but the context is sufficient for a typical use case.

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