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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds valuable details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This goes beyond the schema and helps the agent understand data preprocessing behavior, though it does not mention edge cases like entirely non-numeric columns or error handling.

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 sentence that front-loads the core purpose (list of statistics) and then adds important handling details. There is no fluff or redundant information; every clause adds value.

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 has a simple input (one column) but no output schema, so the description should clarify the return format. It lists the statistics but does not specify the order, structure, or whether the 'count' of non-numeric rows is included in the output. The phrase 'excluded and counted' is ambiguous—does it mean the count of excluded rows is reported? This lack of clarity makes it incomplete for a tool that produces a structured result.

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 description coverage is 0%, so the description must compensate. It does clarify that the 'column' parameter should be a numeric column of the LLC Lane dataset, which adds meaning beyond the schema's simple type constraint. However, it does not elaborate on accepted formats or whether the column must exist, leaving some gaps.

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 tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of a specific dataset (LLC Lane). It uses a specific verb and resource, and the list of statistics distinguishes it from sibling tools like dataset_row and dataset_search, which focus on retrieval rather than aggregation.

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 clearly implies when to use this tool: when summary statistics for a numeric column are needed. It provides context (numeric column, specific dataset) but does not explicitly mention alternatives or when not to use it. However, the specialized nature of the tool makes the usage context clear enough.

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