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

With no annotations, the description carries the full behavioral burden and does add value: it discloses that grouping commas and currency symbols are handled during parsing, and that non-numeric rows are excluded and counted. This is useful beyond what the schema provides. It does not cover edge cases like empty columns or nonexistent column names, but the disclosed parsing behavior is the main practical risk.

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, well-structured sentence: the statistics are front-loaded, and the parsing caveats are compactly placed in a parenthetical. Every element earns its place with no filler or redundancy.

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 one-parameter tool, the description is nearly complete: it enumerates the six computed outputs, which effectively conveys the return content despite the lack of an output schema. Minor gaps are how to discover valid column names and behavior when all rows are non-numeric or the column is missing, but these are minor at this complexity level.

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 for the single parameter. It clarifies that the column should be numeric, but it does not explicitly state that 'column' is a column name or how to obtain a valid value (e.g., via dataset_columns). The description adds some meaning over the bare string schema but leaves the parameter semantics partly to inference.

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 resource ('numeric column of the Funnelvo dataset') and the exact computation performed (count, min, max, mean, median, sum). The enumerated statistics clearly distinguish it from siblings like dataset_search and dataset_row, although no sibling is named explicitly.

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?

Usage context is implied: the tool is for computing summary statistics on a numeric column. However, there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives such as dataset_top or dataset_columns for discovering valid columns.

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