correlation_matrix

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

With no annotations, the description carries the full burden and meets it: it discloses the default method (pearson), ranking by absolute correlation, the 0.9 threshold for high_correlation_pairs, the conditional full-matrix output for 15 or fewer numeric columns, and the exclusion/listing of constant columns.

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 front-loaded with a one-line summary and expands into compact, purposeful detail. Every sentence adds value—method options, ranking behavior, threshold, matrix condition, constant columns, and usage context—without padding.

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?

Given no output schema, the description explains return values well (pairs, high_correlation_pairs, conditional matrix, constants). However, max_rows is never addressed, so its effect is unclear; this is a small gap in an otherwise complete profile.

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. It adds strong semantics for method (pearson/spearman/kendall) and column (target-variable ranking), but it does not explain path or max_rows, leaving two of four parameters under-specified.

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 opens with a specific verb and resource—'Correlations between numeric columns, ranked by strength'—and then explains pairwise computation, ranking, and optional target-column behavior. This clearly differentiates correlation_matrix from the profiling, preview, and stats sibling tools.

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?

It explicitly states when to use it: 'when the user is selecting features for a model, hunting redundant columns, or asking what moves together with a numeric outcome.' It does not explicitly name alternatives to avoid, but the guidance is clear and actionable.

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