column_statistics

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

No annotations are provided, so the description bears full burden for behavioral disclosure. It indicates the tool returns a JSON object with specific stats but does not state whether the operation is read-only, whether it modifies the worksheet, or any required permissions. No caveats about missing data or edge cases are mentioned.

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 extremely concise: one sentence stating the purpose followed by a bullet list of returned fields. No unnecessary words, and the key action (returns descriptive statistics) is front-loaded.

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?

Although an output schema is indicated as present, it is not provided, so the description's listing of return fields compensates partially. However, the tool has three parameters with no explanatory text, and the description omits behavioral context like read-only status or data type requirements. For a tool with no annotations and minimal schema coverage, the description falls short of being fully self-contained.

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?

With 0% schema description coverage, the description provides no additional meaning for the three required parameters (data_book, data_sheet, col). While parameter names are suggestive, the description does not clarify expected formats, units, or how the column is identified (e.g., index vs. name). This forces the agent to rely solely on the minimal 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 clearly states it provides descriptive statistics for one worksheet column, listing the specific measures returned (mean, sd, se, variance, median, min, max, sum, n). This distinguishes it from sibling tools like compare_means or curve_fit, which serve different analytical 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 description lacks guidance on when to use this tool versus alternatives. It does not specify prerequisites (e.g., numeric column), exclusions, or mention related tools for multi-column or grouped statistics. The user must infer usage context from the tool name alone.

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