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 reveals that grouping commas and currency are handled, non-numeric rows are excluded and counted, and it lists the returned statistics. This goes beyond a simple 'compute stats' and provides meaningful context. However, it does not disclose error handling for invalid columns or empty datasets, which prevents a 5.

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 sentence that front-loads the list of statistics and then provides two parenthetical clarifications. It is efficient and avoids redundancy, though the parenthetical about number handling is somewhat dense. Overall, it is appropriately sized and well-structured for its purpose.

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?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is largely complete. It explains what stats are returned, how numeric formatting is normalized, and how non-numeric rows are treated. Missing details include potential error messages or behavior if the column does not exist or contains no numeric values, but these are minor for a simple stats tool. The absence of an output schema is partially mitigated by listing the returned stats.

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 only parameter, 'column', is documented only as a string in the schema with no description (0% coverage). The tool description adds crucial meaning by specifying it must be a numeric column and that formatting (commas, currency) is handled. This compensates for the schema's silence and helps the agent understand what value to provide, though it could be more explicit about accepted formats.

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 tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Yieldzo dataset. The verb 'compute' is implied, the resource is named, and the specific stats are listed. This distinguishes it from siblings like dataset_row (returns rows) and dataset_search (searches) without ambiguity.

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 implies usage for numeric column statistics but does not explicitly state when to prefer this over alternatives. It does not mention exclusions or conditions like 'use this instead of dataset_top when you need aggregate measures.' However, the clear purpose partially compensates, so a 3 is appropriate for lacking explicit routing guidance.

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