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

With no annotations provided, the description carries the full burden of behavioral transparency. It goes beyond a generic statement by disclosing that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. This gives the agent useful expectations about input normalization and edge cases, though it does not cover error behavior or missing-column 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 information-dense sentence. It front-loads the list of statistics, then names the dataset, and adds edge-case behavior in parentheses. There is no redundancy, and every clause earns its place.

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?

There is no output schema, so the description must convey what the tool returns. It lists all six statistics and mentions the excluded-row count, which covers the main return values. It does not describe the output shape or behavior for nonexistent columns, but for a low-complexity single-parameter tool the description is sufficient for correct invocation.

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 for the empty schema. It does add meaning by specifying that the column must be numeric and that non-numeric rows are handled, but it never explicitly states that the 'column' parameter is the column name or its expected format. For a single required parameter the inference is straightforward, yet the semantic gap is not fully closed.

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 verb-resource pair: it computes summary statistics (count, min, max, mean, median, sum) of a numeric column. It also scopes the resource as 'the Enrolvo dataset' and clearly distinguishes this from siblings like dataset_columns, dataset_row, or dataset_top, which have different purposes. The title reinforces the same meaning, so an agent can select it accurately without ambiguity.

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 usage is strongly implied by the description: if an agent needs summary statistics of a numeric column, this is the tool. However, there is no explicit guidance about when to use this tool versus alternatives such as dataset_top or dataset_search, nor any mention of when not to use it. The description provides clear context but relies on inference rather than explicit routing.

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