List registry-backed SPSS methods available for structured execution. Use this to discover cold methods that have schemas, templates, and coverage assertions.

Get the JSON schema for a registry-backed SPSS method. Useful for structured orchestration and parameter inspection before execution.

Get support metadata for a registry-backed SPSS method, including command family, support tier, coverage assertions, and documentation tags.

Check the SPSS MCP server status: which capabilities are available (SPSS installed vs file-only mode), SPSS path, library versions, and configuration. Call this first to understand what tools are available.

List SPSS .sav files in a directory. Useful for discovering available datasets when the user hasn't specified a file path.

List all variable names and their labels from an SPSS .sav file. Optionally filter by a search term. Does not require SPSS to be installed.

Read variable names, types, labels, and value labels from an SPSS .sav file. Returns a detailed Markdown report of the file's structure. Does not require SPSS to be installed.

Read rows of data from an SPSS .sav file as a Markdown table. Optionally filter to specific variables and limit row count. Does not require SPSS to be installed.

Get a summary of an SPSS .sav file: case count, variable count, variable list, and basic descriptive statistics computed locally (no SPSS needed). Does not require SPSS to be installed.

Convert a CSV file to SPSS .sav format directly using pandas + pyreadstat — no IBM SPSS Statistics installation required. Much faster than going through SPSS syntax because it bypasses the SPSS engine entirely. Saves the .sav file next to the CSV by default, or to a custom output_path.

Execute arbitrary SPSS syntax commands and return the output as Markdown. Optionally specify a data_file to automatically prepend GET FILE. By default, this also persists .spv (SPSS viewer) and .sps (executed syntax) files. Requires IBM SPSS Statistics to be installed.

Run SPSS FREQUENCIES on one or more variables. Returns frequency tables with counts, percentages, and optional statistics. Requires IBM SPSS Statistics to be installed.

Run SPSS DESCRIPTIVES for numeric variables. Returns N, mean, std deviation, min, max, and optional statistics. Requires IBM SPSS Statistics to be installed.

Run SPSS CROSSTABS to create a contingency table between two categorical variables. Optionally includes chi-square test and row/column percentages. Requires IBM SPSS Statistics to be installed.

Run SPSS linear regression. Specify a dependent variable and one or more predictors. Returns coefficients, R-squared, ANOVA table, and significance tests. Requires IBM SPSS Statistics to be installed.

Run SPSS t-test. Supports one_sample, independent, and paired test types. Requires IBM SPSS Statistics to be installed.

Run SPSS one-way ANOVA (ONEWAY). Optionally includes post-hoc tests (e.g., TUKEY, BONFERRONI, LSD). Requires IBM SPSS Statistics to be installed.

Run SPSS CORRELATIONS to compute Pearson or Spearman correlation matrix. Requires IBM SPSS Statistics to be installed.

Run SPSS FACTOR analysis (principal components or principal axis factoring). Includes eigenvalues, variance explained, and rotated factor matrix. Requires IBM SPSS Statistics to be installed.

Run SPSS RELIABILITY analysis (Cronbach's alpha). Returns scale reliability and item statistics for psychometric workflows. Requires IBM SPSS Statistics to be installed.

Compute a scale score (SUM or MEAN) from multiple item variables, with optional reverse coding and minimum valid item count. Requires IBM SPSS Statistics to be installed.

Run common nonparametric tests in SPSS: Mann-Whitney U, Wilcoxon signed-rank, or Kruskal-Wallis. Requires IBM SPSS Statistics to be installed.

Run SPSS EXAMINE to check normality and outliers for numeric variables, with optional diagnostic plots. Requires IBM SPSS Statistics to be installed.

Run SPSS repeated-measures ANOVA (within-subject GLM). Provide within-factor name, number of levels, and one variable per level. Requires IBM SPSS Statistics to be installed.

Validate SPSS syntax without executing it. Checks for basic syntax errors. Requires IBM SPSS Statistics to be installed.

Run binary or multinomial logistic regression. Supports stepwise selection, categorical predictors, and model diagnostics. Requires IBM SPSS Statistics to be installed.

Run ordinal regression (PLUM) for ordered categorical outcomes. Supports multiple link functions and parallel lines test. Requires IBM SPSS Statistics to be installed.

Run generalized linear model (GENLIN) with flexible distribution and link functions. Supports Poisson, binomial, gamma, negative binomial, and other distributions. Requires IBM SPSS Statistics to be installed.

Run linear mixed-effects model (multilevel model) with random effects. Supports nested and crossed random effects, repeated measures structures. Requires IBM SPSS Statistics to be installed.

Run generalized linear mixed model combining GLM with random effects. Supports non-normal outcomes with hierarchical structure. Requires IBM SPSS Statistics to be installed.

Run Cox proportional hazards regression for survival analysis. Supports time-dependent covariates, stratification, and model diagnostics. Requires IBM SPSS Statistics to be installed.

Run Kaplan-Meier survival analysis with log-rank test. Produces survival curves and compares groups. Requires IBM SPSS Statistics to be installed.

Run discriminant analysis to classify cases into groups. Supports stepwise selection and cross-validation. Requires IBM SPSS Statistics to be installed.

Run hierarchical cluster analysis with dendrogram. Supports multiple linkage methods and distance measures. Requires IBM SPSS Statistics to be installed.

Run two-step cluster analysis with automatic cluster number determination. Handles large datasets and mixed variable types. Requires IBM SPSS Statistics to be installed.

Run multivariate analysis of variance (MANOVA) for multiple dependent variables. Tests multivariate effects and provides univariate follow-ups. Requires IBM SPSS Statistics to be installed.

Run univariate general linear model (GLM) with factorial designs. Supports estimated marginal means, contrasts, and post-hoc tests. Requires IBM SPSS Statistics to be installed.

Export a publication-ready histogram (PNG/TIFF/EMF) via GGRAPH + OMS IMAGE. Provide the continuous variable; the chart file path is returned for direct submission.

Export a publication-ready scatter plot (PNG/TIFF/EMF) via GGRAPH + OMS IMAGE. Provide x and y variables; the chart file path is returned for direct submission.

Export a publication-ready bar chart (PNG/TIFF/EMF) of a categorical variable against the mean (or sum) of a continuous variable, via GGRAPH + OMS IMAGE.

Export a publication-ready line chart (PNG/TIFF/EMF) via GGRAPH + OMS IMAGE. Provide the x (time/ordinal) and y variables; the file path is returned.

Export a publication-ready box-and-whisker plot (PNG/TIFF/EMF) via GGRAPH + OMS. Provide a continuous variable and optionally a categorical grouping variable.

Export a publication-ready error bar chart (PNG/TIFF/EMF): mean with confidence-interval whiskers per category, via GGRAPH + OMS.

Export a publication-ready normal Q-Q plot (PNG/TIFF/EMF) of a continuous variable via PPLOT, captured through the OMS chart pipeline.

Export a publication-ready Kaplan-Meier survival curve (PNG/TIFF/EMF) via the KM procedure. Provide time and status variables; group is optional.

Export a publication-ready area chart (PNG/TIFF/EMF) of y against a time/ordinal x variable, via GGRAPH + OMS.

Export a publication-ready histogram overlaid with a normal density curve (PNG/TIFF/EMF) via GGRAPH + OMS. Provide a continuous variable.

Export a publication-ready bar chart of category means with confidence-interval error bars (PNG/TIFF/EMF) via GGRAPH + OMS.

Run SPSS syntax and return a unified structured result as JSON: {markdown, json: {tables, summary}, files, warnings}. The summary extracts key statistics (t/F/r/B/p) for t-test, ANOVA, correlation, regression, descriptives and frequencies.

Run a Baron & Kenny three-step mediation analysis (X -> M -> Y) with regression, reporting paths a/b/c/c', the indirect effect a*b, and a Sobel test. Does not bundle the PROCESS macro.

Run a mean-centred moderation regression (Y ~ X + W + X*W) and report the interaction term that tests the moderating effect of W.