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sergiudanstan

R MCP Server

by sergiudanstan
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Python
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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

Related MCP server: Skill Management MCP Server

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

This server cannot be installed

A
license - permissive license
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

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