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

With no annotations, the description carries the behavioral disclosure burden. It discloses important data-cleaning behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. This goes beyond a generic 'computes statistics' statement and gives the agent useful expectations about edge cases.

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, information-dense sentence that front-loads the returned statistics and adds edge-case handling in a parenthetical. No words are wasted and every clause contributes useful information.

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 one-parameter read-only tool, the description covers the input semantics and the list of computed values. However, with no output schema, it does not state the exact return shape (e.g., whether the result is an object with those keys), and it omits failure behavior such as what happens when the column does not exist or contains no numeric rows.

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?

Since schema description coverage is 0%, the description must compensate for the bare 'column' parameter. It adds semantics by explaining that the column must be numeric, that formatting like commas and currency is normalized, and that non-numeric values are excluded. This is meaningful interpretation beyond the schema.

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 a concrete operation—computing summary statistics for a numeric column—and lists exactly which statistics are returned: count, min, max, mean, median, and sum. This uniquely distinguishes it from siblings like dataset_row, dataset_search, and dataset_compare, which serve different purposes.

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 intended use is implied clearly: when an agent needs numeric summary statistics for a column in the Turndownly dataset. However, it does not explicitly state when NOT to use this tool or point to an alternative for other types of queries, so the routing guidance is implicit rather than explicit.

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