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

With no annotations, the description carries the behavioral disclosure burden. It goes beyond a bare 'compute stats' statement by noting that grouping commas and currency are handled and that non-numeric rows are excluded and counted. This gives the agent meaningful expectations about data cleaning behavior.

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?

A single sentence that front-loads the computed statistics and appends important edge-case handling. Every clause adds information and there is no filler.

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 single-parameter tool with no output schema, the description is largely complete: it lists all computed values and describes how dirty data is treated. The main gap is absence of usage alternatives, which is already penalized under usage_guidelines, so the remaining detail is sufficient for an agent to call it correctly.

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 add meaning by specifying that the column must be a numeric column of the DrawScheduleWorks dataset and that formatted values are normalized. However, it does not clarify how column names are passed, what valid column identifiers look like, or whether the column should match dataset_columns output exactly.

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 operation: computing count, min, max, mean, median, and sum for a numeric column. It is specific about the resource (DrawScheduleWorks dataset) and naturally distinguishes itself from siblings like dataset_top or dataset_search by describing statistical aggregation rather than retrieval or comparison.

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?

There is no explicit guidance about when to use this tool versus alternatives like dataset_top or dataset_search. The context is implied—use it when summary statistics are needed—but no exclusions, prerequisites, or alternative selection criteria are provided.

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