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

With no annotations provided, the description carries the full behavioral burden. It discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which is genuinely useful. It does not describe the exact response structure or edge cases like an all-non-numeric column, but the main behaviors are transparent.

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 entire definition is a single, well-structured sentence that front-loads the computed statistics, then adds the dataset scope and data-cleaning behavior in a parenthetical. There is no redundancy with the schema and no wasted words.

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 tool with only one parameter and no output schema or annotations, the description covers what is computed, on which dataset, and how malformed values are treated. A small gap remains around the exact return format and edge-case behavior, but the definition is largely complete for a simple stats tool.

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 input schema only says the parameter is a non-empty string. The description adds meaningful semantics: the column must be a numeric column of the Perdiemo dataset and will be cleaned before computation. It could have mentioned how to discover valid column names, but it provides substantial guidance beyond the schema.

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 specifies the resource (a numeric column of the Perdiemo dataset), the operation (summary statistics), and explicitly enumerates the outputs: count, min, max, mean, median, and sum. This clearly distinguishes it from sibling tools like dataset_columns, dataset_row, or dataset_search.

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?

It clearly implies when to use the tool: when summary statistics of a numeric column are needed. It also explains how dirty numeric values are handled. However, it does not explicitly name alternative sibling tools or state when not to use it, so it falls short of a 5.

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