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

With no annotations provided, the description carries the full burden and does real work: it discloses that grouping commas and currency symbols are parsed, and that non-numeric rows are excluded and counted. These are the key non-obvious behaviors for a stats tool over a messy dataset, though it stops short of covering null handling or behavior when the entire column is non-numeric.

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?

One dense sentence that front-loads the computed statistics and appends the two data-handling caveats in a parenthetical. Every element earns its place with no filler or redundancy.

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 low-complexity tool (one parameter, no nested objects) with no annotations and no output schema, the description covers purpose, parameter semantics, and preprocessing behavior; the output shape is reasonably inferable from the enumerated stats. The main gap is edge-case behavior (missing column, all non-numeric column) and exact output format.

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?

Schema coverage is 0% and the schema only states 'column' is a non-empty string, so the description must add meaning. It does: 'column' is a numeric column of the Kickoffo dataset, which is the essential semantic. It does not specify whether names are case-sensitive or must match dataset_columns output, but for a single simple parameter this is adequate.

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 action (computes count, min, max, mean, median, sum) on a defined resource (a numeric column of the Kickoffo dataset), leaving no ambiguity about what the tool does. The enumerated statistics clearly differentiate it from siblings like dataset_columns, dataset_top, and dataset_search, which serve other 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?

Usage is implied: call this tool when you need summary statistics for a numeric column of the dataset. However, it does not explicitly state when not to use it or name alternatives (e.g., dataset_top for top rows, dataset_search for row-level queries), so an agent must infer the selection from the enumerated stats alone.

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