Summarize a single numeric series with descriptive statistics. Returns a markdown report: 'summary' gives count/min/max/mean/sum; 'stats' adds median, quartiles, standard deviation, variance, and coefficient of variation. Accepts a number[] or an array of objects (the first numeric property is used). For multi-column tables or cross-variable correlation use advanced_statistical_analysis; to transform values use advanced_data_preprocessing.

Rank options against weighted criteria with a weighted-sum decision matrix. Returns a markdown report: ranked options, a per-option breakdown (score × weight contribution, strengths, weaknesses), and a recommendation. Weights are normalized to sum to 1; omit them for equal weighting.

Fit a regression model (linear, polynomial, logistic, or multivariate) predicting a named dependent variable from named predictor columns. Returns a markdown report with fitted coefficients, performance metrics, and interpretation. Use this when you have a designated outcome to predict; for association strength without a model use advanced_statistical_analysis, and to score existing predictions use ml_model_evaluation.

Run a statistical hypothesis test and report the p-value with a reject / fail-to-reject decision at the chosen alpha. Supports independent (Welch) and paired t-tests, Pearson-correlation significance, chi-square independence, and one-way ANOVA. Returns a markdown report with the test statistic, p-value, and conclusion. Use this when you need significance; for descriptive correlation without inference use advanced_statistical_analysis.

Generate a chart specification (Vega-Lite) plus rendering instructions for a dataset — it describes a chart, it does not render an image. Supports scatter, line, bar, histogram, box, heatmap, pie, violin, and correlation plots. Returns a markdown report with the data-point count, the spec, and usage guidance.

Assess a natural-language argument for structure, validity, strength, and fallacies. Returns a markdown analysis; 'comprehensive' (default) runs all four plus optional improvement recommendations. Use this for overall argument quality; to only flag and name fallacies use logical_fallacy_detector.

Detect and name logical fallacies in text via pattern matching, each with a confidence score, description, and before/after examples. Returns a markdown report grouped by category with an overall severity assessment. Use this to flag specific fallacies; for a full argument assessment use logical_argument_analyzer.

Generate alternative viewpoints on a problem — by stakeholder or discipline — grounded in web research via Exa. Returns a markdown report with key facts and actionable insights per perspective. Requires EXA_API_KEY and ENABLE_RESEARCH_INTEGRATION=true and makes live network calls; it fails without them. To cross-check factual claims instead of generating viewpoints, use verify_research.

Compute per-column descriptive statistics, or Pearson correlation for every numeric column pair, over a table of records. Returns a markdown report (mean/median/std/variance/min/max per column, or r plus a weak/moderate/strong label per pair); non-numeric columns are ignored. Use analyze_dataset for a single numeric series; for correlation significance (p-values) use hypothesis_testing; to fit a predictive model use advanced_regression_analysis.

Transform a numeric series for downstream modeling: min-max normalization, z-score standardization, missing-value handling, or IQR outlier detection. Returns a markdown report with the transform's parameters and a preview of the resulting values. Use analyze_dataset to describe data without changing it.

Score an existing model's predictions against actual values. Classification returns accuracy/precision/recall/F1 from a binary (0/1) confusion matrix; regression returns MSE/MAE/RMSE/R². Returns a markdown report of the requested metrics plus sample count. This scores supplied predictions; to fit a model from raw data use advanced_regression_analysis.

Cross-verify a factual claim across multiple web sources via Exa and return a structured confidence verdict. Returns an object {verifiedResults, confidence:{score, verified, consistencyThreshold, details:{sourceCount, uniqueSources, conflictingClaims, ...}}} — not markdown — from cross-source Jaccard consistency and conflict detection. Requires EXA_API_KEY and ENABLE_RESEARCH_INTEGRATION=true and makes live network calls; it fails without them. To generate alternative viewpoints instead of verifying facts, use perspective_shifter.