R MCP Server

R MCP 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