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get_correlations

Compute pairwise Pearson and Spearman correlations for numeric columns, generate a heatmap, and create scatter plots for correlations above a configurable threshold.

Instructions

Compute pairwise correlations between all numeric columns in the dataset and generate a Spearman correlation heatmap. Scatter plots are generated for column pairs with a Spearman correlation above the threshold.

Returns both Pearson and Spearman correlation matrices, the strongest pairs above the threshold (sorted by absolute Spearman correlation, max 10), highly correlated flags for pairs with |ρ| >= 0.9, and file paths for all generated plots.

Only continuous and discrete columns are included — categorical, binary, temporal, and high_cardinality columns are excluded automatically.

threshold controls which pairs get scatter plots (default 0.5). Set higher e.g. 0.7 for only strong correlations, lower e.g. 0.3 to cast a wider net. Scatter plots are capped at 10 pairs regardless of threshold.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
output_dirNo
thresholdNo
tableNo
Behavior5/5

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

With no annotations, description fully discloses behavioral traits: returns both Pearson and Spearman matrices, strongest pairs (max 10), flags for high correlation, generated plot paths, scatter plot cap (10 pairs), and automated column type exclusions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is moderately sized with several sentences, but each adds necessary detail. Well-structured: first paragraph overview, second paragraph return values, third paragraph exclusions, fourth paragraph parameter guidance. Could be slightly more concise, but no wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all key aspects: what tool computes, parameters, returns, exclusions, and parameter behavior. With 4 params, 0% schema coverage, and no output schema, it provides sufficient information for an agent to use effectively. Minor gaps in file_path format, but overall complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, so description must explain parameters. It clarifies threshold behavior with examples and mentions file_path (implied input), output_dir, and table. Does not detail file_path format or output_dir structure, but adds significant value over bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it computes pairwise correlations and generates Spearman heatmap and scatter plots. It distinguishes from sibling tools (generate_report, get_all_summaries, etc.) which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context on when to use (numeric columns) and exclusions (categorical, binary, etc.). Also gives practical advice on threshold adjustment. Lacks explicit when-not-to-use, but overall strong guidance.

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

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