R Server

An MCP (Model Context Protocol) server that lets AI assistants execute R code, create visualizations, analyze data, and manage packages — all through a local Rscript CLI.

Features — 62 Tools

Execution (3 tools)

Tool	Description
evaluate_r_code	Execute inline R code and return console output
run_r_file	Run an .R script file
run_r_test_file	Run testthat tests and report pass/fail

Visualization (5 tools)

Tool	Description
create_r_plot	Execute base R plotting code and save as PNG
create_ggplot	Create ggplot2 plots with auto-theme and save as PNG
create_correlation_heatmap	Generate a correlation heatmap from a data file
create_multi_plot	Arrange multiple ggplots into a multi-panel figure
render_rmarkdown	Render .Rmd files to HTML or PDF

Statistical Analysis (5 tools)

Tool	Description
fit_linear_model	Fit lm/glm and return coefficients, R-squared, p-values
correlation_matrix	Compute correlation matrix with p-values
hypothesis_test	Run t-test, Wilcoxon, chi-squared, Shapiro-Wilk, etc.
descriptive_stats	Per-column mean, sd, quartiles, skewness, kurtosis
pca_analysis	Principal Component Analysis with loadings and variance

Data Wrangling (5 tools)

Tool	Description
read_data	Read CSV, TSV, Excel, JSON, Parquet, or RDS files
write_data	Execute R code and save results to CSV/TSV/RDS/JSON
reshape_data	Pivot data between wide and long formats (tidyr)
merge_datasets	Join two data files (inner, left, right, full)
generate_sample_data	Load built-in R datasets (mtcars, iris, etc.) as CSV

Time Series (4 tools)

Tool	Description
forecast_timeseries	Fit ARIMA/ETS/TBATS/Holt-Winters and forecast with plot
decompose_timeseries	Decompose into trend, seasonal, and remainder (STL/classical)
stationarity_test	Unit root tests — ADF, KPSS, Phillips-Perron
acf_pacf_plot	Plot ACF and PACF side by side with significance bounds

Clustering (2 tools)

Tool	Description
kmeans_clustering	K-means with elbow plot, silhouette score, PCA projection
hierarchical_clustering	Hierarchical clustering with dendrogram and cophenetic correlation

Advanced Statistics (7 tools)

Tool	Description
anova_test	One-way and two-way ANOVA with post-hoc tests
mixed_effects_model	Fit linear mixed-effects models (lme4)
bootstrap_ci	Bootstrap confidence intervals for any statistic
normality_tests	Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov, Lilliefors
outlier_detection	Grubbs, Dixon, Rosner, IQR, and Z-score methods
quantile_regression	Fit quantile regression at specified quantiles
survival_analysis	Kaplan-Meier survival curves and Cox proportional hazards

Interactive & Publication Plots (5 tools)

Tool	Description
create_plotly	Create interactive plotly visualizations saved as HTML
create_publication_plot	Publication-ready plots using ggpubr
create_corrplot	Correlation matrix visualization (corrplot package)
create_paired_comparison_plot	Group comparisons with statistical significance
create_diagnostic_plots	Regression diagnostic plots (residuals, Q-Q, Cook's distance)

Probability Distributions (5 tools)

Tool	Description
distribution_calculator	Compute d/p/q/r for 16 distributions (normal, binomial, t, F, chi-sq, etc.)
distribution_plot	Histogram of random samples with theoretical density overlay
random_sample	Sample from any population with/without replacement
qq_plot	Q-Q plot to assess distributional fit with Shapiro-Wilk test
simulate_clt	Central Limit Theorem simulation for any distribution

Proportion & Contingency Tests (5 tools)

Tool	Description
proportion_test	One-sample and two-sample proportion tests (prop.test)
binomial_test	Exact binomial test for small samples
chi_squared_test	Chi-squared test for goodness of fit, independence, homogeneity
fisher_test	Fisher's exact test on 2x2 contingency tables
contingency_table	Create contingency table with mosaic plot and chi-squared test

Regression & Post-hoc (6 tools)

Tool	Description
robust_regression	Robust regression (MASS::rlm/lqs) resistant to outliers
polynomial_regression	Fit and compare polynomial models of different degrees
predict_with_ci	Predictions with confidence and prediction intervals
tukey_hsd	Tukey's HSD post-hoc pairwise comparisons after ANOVA
kruskal_wallis_test	Kruskal-Wallis nonparametric test for group differences
power_analysis	Compute sample size or power for t-test and proportion test

Exploratory Data Analysis (5 tools)

Tool	Description
pairs_plot	Scatterplot matrix with correlations and histograms
density_plot	Kernel density estimation plot with multiple kernels
ecdf_plot	Empirical CDF plot with optional normal overlay
stem_and_leaf	Text-based stem-and-leaf display with five-number summary
variance_test	F-test, Bartlett's, and Fligner-Killeen variance equality tests

Utilities (5 tools)

Tool	Description
check_r_code	Static analysis via lintr
get_data_summary	Load CSV/TSV/RDS and return summary stats
detect_r_packages	List all installed R packages
get_r_version	Return R version and session info
install_r_package	Install a CRAN package

Prerequisites

• R (>= 4.0) with Rscript on your PATH
• Python (>= 3.10)

Install R from CRAN or via Homebrew:

brew install r

Installation

git clone https://github.com/sergiudanstan/r-mcp.git
cd r-mcp
pip install -e .

Usage

With Claude Code

Add to your Claude Code MCP settings (~/.claude/settings.json):

{
  "mcpServers": {
    "r": {
      "command": "python",
      "args": ["-m", "r_mcp"],
      "cwd": "/path/to/r-mcp"
    }
  }
}

Standalone

python -m r_mcp

The server communicates over stdio using the MCP protocol.

How It Works

The server wraps the Rscript --vanilla CLI. Each tool call spawns a fresh R session, executes the code, and returns structured JSON results. Code is wrapped in tryCatch for clean error reporting.

• Workspace: Output files (plots, rendered docs) are saved to ~/r-mcp-workspace/
• Timeout: Default 60s per execution (configurable per call)
• Safety: Path traversal prevention on file outputs; output truncation at 50K chars

Examples

Run R code

# Via the evaluate_r_code tool
x <- rnorm(100)
cat("Mean:", mean(x), "\nSD:", sd(x), "\n")

Create a plot

# Via the create_r_plot tool
library(ggplot2)
df <- data.frame(x = rnorm(200), y = rnorm(200))
ggplot(df, aes(x, y)) + geom_point(alpha = 0.5) + theme_minimal()

Probability distributions

# Via the distribution_calculator tool
# Compute P(X <= 1.96) for standard normal
pnorm(1.96, mean=0, sd=1)

# Via the distribution_plot tool
# Visualize chi-squared(5) distribution with 1000 samples

Hypothesis testing

# Via the proportion_test tool
# Test if 42 out of 100 differs from 50%
prop.test(42, 100, p = 0.5)

# Via the hypothesis_test tool
# Two-sample t-test
t.test(x, y, alternative = "two.sided")

Analyze a CSV

Use get_data_summary with a file path to get dimensions, column types, summary statistics, and a preview.

License

MIT