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

With no annotations, the description carries the full burden. It discloses that non-numeric rows are excluded and counted, and that grouping commas and currency are handled, which are key behavioral traits. However, it does not explicitly state that the tool is read-only (obvious but not stated) or describe error behavior for missing columns, though these are minor for a stats tool.

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, information-dense sentence. It front-loads the statistics list, then adds data-handling caveats. No filler or redundancy; every clause contributes value. Efficient and well-structured.

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 tool with one parameter and no output schema, the description covers the essential behavior: what statistics are computed and how data quirks are handled. The list of stats effectively implies the return structure. It lacks explicit details on error cases (e.g., missing column) but is otherwise adequate for an agent to call it correctly.

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 (0% coverage). The description adds meaning by indicating the column must be numeric and that formatting like commas and currency is handled. This clarifies the expected input beyond the bare schema, though it does not explicitly define the column name format or validation rules.

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 states a specific verb (compute), a resource (numeric column of the Vatnix dataset), and the exact set of statistics (count, min, max, mean, median, sum). It also adds data-handling details (commas/currency, non-numeric rows) that clearly distinguish it 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 purpose is clear, but there is no explicit guidance on when to use this tool versus the sibling tools (e.g., dataset_row, dataset_top). Usage is implied by the stats focus, but no exclusions or alternative recommendations are given. A brief note like 'Use this when you need aggregate statistics, not raw rows' would improve it.

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