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

With no annotations, the description carries the full burden and does disclose non-obvious data-handling behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded but counted. This matters because the agent learns results may be computed over a subset. It does not cover permissions, failure modes, or the shape/ordering of the returned statistics.

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?

A single dense sentence that front-loads the returned statistics before the scoping and data-cleaning caveats. No filler, though the parenthetical packs three distinct behaviors and could be split for easier scanning.

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, no-output-schema tool with no annotations, the description covers the returned metrics and the data-cleaning rules, which is most of what an agent needs. Missing only explicit routing to sibling tools and any statement about output ordering or edge cases like empty result sets.

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 description coverage is 0%, so the schema only says 'string, minLength 1' and provides no semantic constraint. The description compensates by making clear the column must hold numeric data (with currency/comma handling) and that non-numeric rows are excluded, which is the key precondition for a correct call.

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?

States a specific computation (count, min, max, mean, median, sum) over a clearly bounded resource (a numeric column of the Answering Service Quotes dataset). The 'numeric column' scoping separates it from siblings like dataset_row or dataset_search, though no sibling is named explicitly.

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?

There is no explicit when-to-use statement or named alternative among the nine dataset_* siblings. Usage is only implied by the requirement that the target column be numeric and by the note that non-numeric rows are dropped, which tells the agent the tool is inappropriate for text columns.

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