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

With no annotations, the description carries the full burden. It discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is valuable behavioral context. It doesn't mention return format or error cases, but the disclosed details go beyond the schema.

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 list of statistics and then adds handling details. Every word is informative, with no fluff 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 tool with one parameter and no output schema, the description covers the core behavior: what stats are computed, how formatting is handled, and how non-numeric rows are treated. It lacks explicit output format details, but that is often not required. The description is adequate for the tool's simplicity.

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 should compensate. It mentions 'numeric column' but doesn't explicitly clarify the expected format of the 'column' parameter (e.g., exact column name string). The parameter name and type are simple, but the description adds minimal value over the schema for parameter semantics.

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 clearly states the tool computes specific summary statistics (count, min, max, mean, median, sum) for a numeric column of the Deflectvo dataset. This is a specific verb+resource combination that distinguishes it from sibling tools like dataset_columns (listing columns) or dataset_row (fetching a row).

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 usage for obtaining summary statistics on a column but does not explicitly state when to choose this over siblings like dataset_top or dataset_compare. No when-not-to-use conditions are provided, so guidance is only implied rather than explicit.

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