Column statistics

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

With no annotations, the description carries the full burden of disclosing behavior. It lists the statistics computed but does not state whether the tool is read-only, whether it modifies the file, how missing values are handled, or what happens with unsupported file types. This lack of side-effect clarity is a gap.

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 brief and front-loaded, with the key usage directive in the first sentence and the statistical capabilities listed in the second. There is no redundant or filler content.

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?

The description covers what the tool does and what statistics it returns, which is sufficient given the lack of an output schema. It does not describe error cases or edge cases, but the core behavior is adequately specified.

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 path parameter is explained by the description ('spreadsheet or CSV file path'), and the columns parameter has a schema description. However, the sheet parameter has no explanation and is not discussed in the description, so one of three parameters is not adequately documented.

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 identifies the tool's purpose: computing whole-column statistics (count, empty, distinct, min, max, sum, mean, median) from spreadsheet or CSV files. It also distinguishes this tool from ordinary file readers by stating that built-in file readers cannot parse spreadsheets.

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 gives some usage context: use it for spreadsheet or CSV file paths when built-in file readers are unsuitable. However, it does not explicitly contrast this tool with sibling tools like sheet_read, sheet_query, or sheet_find, nor does it state when to prefer one of those alternatives.

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