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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does reveal that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. However, it omits the output format (e.g., JSON structure with field names), error behavior for entirely non-numeric columns, and whether the tool requires the column to exist. These are significant gaps for an unannotated tool.

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, dense sentence that efficiently lists the statistics, specifies the dataset, and includes the two data-cleaning behaviors in parentheses. It front-loads the core purpose and avoids any fluff or repetition, making it highly concise and well-structured.

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 tool is simple (one parameter, no output schema), but the description is not fully complete for an agent. It covers what statistics are computed and how data is cleaned, but it does not specify the return format (e.g., JSON keys) or behavior for edge cases like an empty column or all-non-numeric data. Given no output schema and no annotations, the description should provide more operational detail to be fully actionable.

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, with no description. The description adds that the column must be numeric and that formatting (commas, currency) is handled, which clarifies expected input semantics. This meaningfully compensates for the 0% schema description coverage, though it does not explicitly state that the value should be a column name from the dataset.

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 the operation: computing summary statistics (count, min, max, mean, median, sum) for a numeric column of the BioBricks dataset. It lists specific metrics and mentions handling of formatting and non-numeric rows, which differentiates it from sibling tools like dataset_row or dataset_search that retrieve data rather than aggregate.

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 provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools such as dataset_columns, dataset_top, or dataset_search, nor does it state conditions that would favor one over the other. The intended use case is implied (when summary stats are needed) but not explicitly articulated.

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