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

With no annotations, the description carries the full burden of behavioral disclosure. It honestly discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which are meaningful edge-case behaviors an agent needs to know. It does not describe the exact response shape, but the computed stats themselves are listed.

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 front-loads the result set and then adds the important caveats. Every element earns its place, and there is no redundant or vague 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 one-parameter tool, the description covers the tool's purpose, the parameter's semantic constraint, and the data-handling behavior. It would be slightly stronger with an explicit note about output structure or error behavior, but nothing essential for choosing and invoking the tool is missing.

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%, so the description must compensate. It adds the key semantic that the column must be numeric and that non-numeric values are excluded, but it does not explain how the column name should be specified, whether it must match dataset_columns exactly, or what happens if the column does not exist.

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 names a specific resource (numeric column of the Runsheetly dataset) and enumerates the exact aggregates computed (count, min, max, mean, median, sum). It clearly distinguishes the tool from sibling tools like dataset_top or dataset_search, which serve different lookup/aggregation 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?

The intended use is strongly implied: use this when you need summary statistics for a numeric column. However, there is no explicit guidance about when to prefer sibling tools such as dataset_search or dataset_top, nor any mention of exclusions or prerequisites.

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