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

With no annotations provided, the description properly discloses key behavioral details: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond the title and helps set expectations about data cleaning behavior. It does not cover edge cases like empty columns or invalid column names, but the disclosed quirks are significant and valuable.

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 compact sentence that front-loads the core statistics and then adds important caveats. Every phrase contributes meaning, and there is no filler or redundant repetition of the tool name or title. The structure is efficient and easy to scan.

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 tool with no output schema, the description provides enough context to understand what the tool returns, especially since it explicitly lists the computed values. It also covers the relevant preprocessing behavior. Minor gaps such as error handling for non-numeric or nonexistent columns prevent a perfect score, but the description is largely complete.

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, and schema description coverage is 0%, so the description must carry meaning. The description does add that the column must be numeric and belong to the EOR Compass dataset, which is useful. However, it does not provide examples, valid column name formats, or point to dataset_columns for discovering available columns.

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 specifies a concrete operation: computing summary statistics for a numeric column of the EOR Compass dataset. It enumerates the exact statistics (count, min, max, mean, median, sum), making the tool's purpose unmistakable. This also differentiates it from siblings like dataset_row, dataset_search, and 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 description implies the tool should be used when summary statistics are needed for a column, and the name 'dataset_stats' reinforces this. However, it does not explicitly mention when not to use it, such as relying on dataset_columns for schema information or dataset_top for ranked values. The usage context is clear but no exclusions or alternatives are stated.

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