What relates to a column

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

Beyond the readOnly/idempotent annotations, it discloses the method (Pearson r for numeric, correlation ratio η plus highest/lowest group for categorical), that output is descriptive with no p-values, and that it scales to files of any size. The confirmation-token prerequisite is left to the schema rather than restated, which is the only gap.

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?

Front-loads the motivating question pattern, then the mechanics, then next steps — a logical flow with little waste. It is dense and slightly long for one paragraph, but nearly every clause carries information the agent needs.

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?

With no output schema, the description still conveys what comes back (a ranking by association strength, group extremes for categoricals) and the confirmation workflow. It stops short of describing result shape or ordering/truncation behavior tied to `top`, which would have made it 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 coverage is 80%, so the baseline is 3; the description adds that `target` must be numeric and that `columns` defaults to 'every other column'. It does not clarify what `top` limits (the schema has no description for it either), which keeps it from a 5.

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?

States a specific verb+resource outcome: ranks every other column by strength of relation to a numeric target, with concrete example questions ('what drives revenue?'). It also implicitly separates itself from the sibling `correlation` by being a broad exploratory ranking over all columns rather than a single pairwise computation.

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?

Explicitly frames the when-to-use case (open analytics questions), names the follow-up alternatives (group_summary, correlation, charts, hypothesis test), and states the precondition that the user confirms the target and columns first. It also says to use it inside an explore analysis, giving the agent clear routing.

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