correlation

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

With no annotations provided, the description carries the burden of behavioral disclosure, and it does so well: it reveals the return structure (full matrix plus per-pair tests), the statistics computed, and the partial-correlation behavior of the `control` parameter. It does not mention missing-data handling or plot outputs, but the essential computational behavior is clearly disclosed.

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 two dense sentences: the first front-loads the primary output, and the second covers methods, adjustment, and the non-obvious `control` behavior. Every clause contributes unique information with 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 9-parameter statistical tool with no output schema, the description covers the main result structure, supported methods, and the partial-correlation mechanism, while the schema documents every parameter. It does not address edge cases like missing values or how `plots` and `correlate_with` alter the output, but the core calling context is sufficiently 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 100%, so the baseline is 3. The description adds genuine value beyond the schema by explaining that `control` residualises variables out of every pair to produce partial correlations, and by clarifying the output shape. This is more than the schema's bare 'Variables to partial out' text provides.

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 'Correlations between numeric variables' and immediately lists the outputs (full matrix plus per-pair tests with n, r, p, confidence intervals, magnitude label), so the tool's core function is clear. It does not explicitly distinguish itself from siblings like survey_correlation or regression, but the scope and statistical detail are specific enough to be 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 the tool: whenever pairwise correlations among numeric variables are wanted, with support for Pearson, Spearman, Kendall, and partial correlations. It does not provide explicit when-to-use versus when-not-to-use guidance, nor does it point to alternatives such as survey_correlation for survey-weighted analyses.

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