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 disclosure. It discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. This is useful behavioral context beyond the schema. However, it does not mention whether the tool returns a single object or a list, or whether it errors on an empty column, so it is not a 5.

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 key statistics and dataset context, then adds edge-case handling. Every word earns its place; no filler or repetition.

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 single-parameter tool with no output schema, the description covers the main behavioral aspects: what statistics are computed, how formatting is handled, and how non-numeric rows are treated. It lacks explicit return format details, but the list of statistics implies the output shape. Given the tool's simplicity, this is nearly complete.

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. The description explains what the 'column' parameter refers to (a numeric column of the dataset) and implies it should be a column name. However, it does not specify the exact format of the column name (e.g., case sensitivity, whether it must match dataset_columns output exactly). Baseline 3 is appropriate because the description adds some meaning but not full parameter-level detail.

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 states a specific verb ('count, min, max, mean, median and sum') and a specific resource ('numeric column of the Corp Tax Calculator dataset'), which clearly distinguishes it from sibling tools like dataset_row or dataset_search. It also mentions the dataset by name, making the tool's scope unambiguous.

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 implies when to use this tool: when summary statistics for a numeric column are needed. However, it does not explicitly state when not to use it or mention alternatives like dataset_top or dataset_compare. The context is clear enough for an agent to infer usage, but there is no explicit routing guidance.

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