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

With no annotations, the description carries the behavioral disclosure burden and does it well: it states that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. These are meaningful edge-case behaviors beyond the title. Minor ambiguity remains about how 'count' relates to excluded rows, and no return format is described.

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 delivers the core purpose, the dataset scope, the output list, and the key preprocessing behaviors with no filler. The most decision-relevant information appears up front.

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, the description is near-complete: it defines the input semantics, the dataset, the computed values, and the handling of malformed numeric rows. It could be slightly more complete by specifying the exact return shape or behavior when no rows remain, but these are minor given the low complexity.

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 'column' as a string with minLength 1 and has 0% description coverage. The tool description compensates by clarifying that the parameter must identify a numeric column and that the tool tolerates formatted values like commas and currency. It does not enumerate valid column names, but it gives the agent enough semantic grounding for a single parameter.

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 resource (Card Machine Pricing dataset), the specific action (compute summary statistics), and enumerates the outputs: count, min, max, mean, median, and sum. This distinguishes it from sibling tools like dataset_top or dataset_row.

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 makes it clear that the tool applies to a numeric column of the Card Machine Pricing dataset, which implies when to use it. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, so the usage guidance remains implicit rather than directive.

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