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r-stats-mcp

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r-stats-mcp

test License: MIT Python 3.12+ R 4.0+

An MCP server that gives an LLM the whole of R's statistical toolkit — hypothesis tests, regression, psychometrics, survival analysis, time series and plots — over a persistent R session.

繁體中文說明請見 README.zh-TW.md。

What makes it different

One R session, kept alive. Load a dataset once and every later tool call sees it. Fitted models are saved back into that session under a name, so you can fit a model, diagnose it, compare it against another, and then run arbitrary R on it — all without re-reading the data.

Results written for a reader, not a parser. Tools return formatted markdown tables rather than raw JSON: p-values as <.001, effect sizes with magnitude labels, confidence intervals already assembled. When an assumption is violated the output says what to do about it — a significant Levene's test points you at Welch, sparse expected counts bring in Fisher's exact test automatically.

No dead ends. 28 structured tools cover the common ground, and r_run executes arbitrary R in the same session for everything else.

You: Load survey.csv and check whether the two groups differ on score.

  data_load(path="survey.csv", name="df")
  data_inspect(data="df")
  data_transform(data="df", to_factor=["group"])
  check_assumptions(data="df", variables=["score"], group="group")
  test_ttest(data="df", y="score", group="group", nonparametric=true)

Related MCP server: StatTools

Requirements

Version

Notes

R

≥ 4.0

Must be on PATH, or set R_MCP_RSCRIPT

Python

≥ 3.12

Managed by uv

uv

any

Install

Required R packages: jsonlite and evaluate. Everything else is needed only by the tools that use it, and each tool tells you exactly what to install when something is missing.

install.packages(c("jsonlite", "evaluate"))

For full coverage of every tool:

install.packages(c(
  "ggplot2", "ragg", "corrplot",          # plotting
  "car", "emmeans", "rstatix",            # ANOVA, post-hoc, VIF
  "psych", "GPArotation", "lavaan",       # scales, factor analysis, SEM
  "lme4", "lmerTest",                     # mixed models
  "survival", "forecast", "tseries",      # survival, time series
  "mgcv", "MASS", "nnet",                 # GAM, ordinal, multinomial
  "haven", "readxl", "openxlsx",          # SPSS/Stata/Excel
  "sandwich", "lmtest"                    # robust standard errors
))

Install

Claude Code

claude mcp add r-stats -- uvx --from git+https://github.com/Ian3738/r-stats-mcp r-stats-mcp

Claude Desktop

Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "r-stats": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/Ian3738/r-stats-mcp", "r-stats-mcp"]
    }
  }
}

If the client cannot find uvx, give the absolute path (which uvx).

From a clone

git clone https://github.com/Ian3738/r-stats-mcp
cd r-stats-mcp
uv sync
claude mcp add r-stats -- "$(pwd)/run-server.sh"

Configuration

Variable

Default

Purpose

R_MCP_RSCRIPT

Rscript from PATH

Path to the R executable

R_MCP_WORKDIR

Startup directory (home if /)

Where relative paths in data_load resolve

R_MCP_TIMEOUT

180

Default per-call timeout in seconds

Verify the setup by asking the model to call r_session_info.

Tools

Session and code

Tool

Purpose

r_run

Execute arbitrary R code; returns console output and plots

r_install_packages

Install packages from CRAN

r_session_info

R version, platform, which statistics packages are available

Data

Tool

Purpose

data_load

CSV, TSV, Excel, SPSS, Stata, SAS, RDS, RData, JSON, Parquet

data_builtin

Datasets bundled with R or an installed package

data_list

Datasets and fitted models currently in the session

data_inspect

Types, missing counts, distinct counts, first rows

data_transform

Filter, derive, recode, factor conversion, sort, long/wide reshape

data_export

Write out as CSV, TSV, Excel or RDS

Descriptives and assumptions

Tool

Purpose

describe

n, mean, SD, SE, CI, median, quartiles, skew, kurtosis — optionally by group

frequency_table

Frequency tables and cross-tabulations with row percentages

check_assumptions

Normality, homogeneity of variance, outliers, VIF, residual diagnostics

Hypothesis tests

Tool

Purpose

test_ttest

One-sample, independent and paired t-tests, with Levene, Cohen's d and rank-based equivalents

test_anova

One-way, factorial, ANCOVA, repeated-measures and mixed designs, with effect sizes and post-hoc comparisons

test_categorical

Chi-square independence and goodness-of-fit, Fisher, McNemar, Cramér's V, odds ratios

test_proportion

One-sample and multi-group proportion tests, including the exact binomial

correlation

Pearson, Spearman, Kendall; partial correlations; multiple-comparison adjustment

Regression and models

Tool

Purpose

regression

Linear, logistic, Poisson, negative binomial, ordinal, multinomial, mixed-effects, GAM

model_diagnostics

Residual normality, heteroscedasticity, autocorrelation, VIF, influential cases

model_compare

AIC, BIC, log-likelihood, ΔAIC, plus nested-model tests

model_predict

Estimated marginal means, or predictions on new data

Scales and questionnaires

Tool

Purpose

reliability

Cronbach's α, McDonald's ω, item-total correlations, α-if-dropped

factor_analysis

EFA and PCA with KMO, Bartlett, parallel analysis, rotated loadings, scree plot

sem

lavaan CFA / SEM / path / growth models with fit indices, CR and AVE

mediation

Bootstrap confidence intervals for indirect effects, multiple mediators supported

moderation

Interaction models with ΔR², simple slopes and an interaction plot

Survival and time series

Tool

Purpose

survival_analysis

Kaplan-Meier with log-rank, Cox regression with the proportional-hazards test

time_series

Stationarity tests, STL decomposition, automatic ARIMA/ETS, forecasts

Plotting

Tool

Purpose

plot

ggplot2 histogram, density, box, violin, scatter, line, bar, Q-Q, correlation heatmap

Examples

Regression with model comparison

regression(data="df", dv="score", predictors=["age","sex","group"], save_as="full")
regression(data="df", dv="score", predictors=["age"], save_as="base")
model_compare(models=["base","full"])
model_diagnostics(model="full")

Scale validation

reliability(data="df", items=["q1",...,"q10"], reverse=["q3","q7"], scale_max=5)
factor_analysis(data="df", variables=["q1",...,"q10"])
sem(data="df", type="cfa", model="anxiety =~ q1 + q2 + q3\ndepression =~ q4 + q5 + q6")

Repeated measures

data_transform(data="df", reshape={
  "direction": "long", "value_cols": ["t1","t2","t3"],
  "id_cols": ["id"], "names_to": "time", "values_to": "score"
})
test_anova(data="df", dv="score", within=["time"], id="id", posthoc="bonferroni")

Survival

survival_analysis(data="df", time="days", event="died", group="treatment",
                  type="km", times_of_interest=[180, 365])
survival_analysis(data="df", time="days", event="died",
                  covariates=["age","sex","stage"], type="cox")

How it works

MCP client  ──stdio/JSON-RPC──▶  Python server  ──NDJSON over pipes──▶  Rscript worker
                                 (tool schemas)                          (.GlobalEnv)

Transport. A long-lived Rscript process reads newline-delimited JSON requests and writes sentinel-delimited JSON responses. Plots are captured with the evaluate package — the same machinery knitr uses — so base graphics, ggplot2 and lattice all work, and are returned as inline PNGs.

Namespace hygiene. User objects live in .GlobalEnv; the server's own machinery lives in a separate .rmcp_sys environment. data_list and r_run therefore see only your data and models.

Timeouts. Two layers: R enforces its own limit with setTimeLimit(), and Python applies a hard timeout on the pipe. If the session has to be restarted, the tool says so explicitly rather than silently losing your data.

Failure messages are actionable. Passing a three-level factor to a t-test does not produce a stack trace — it tells you the levels it found and points you at test_anova.

Development

uv sync
uv run python -c "
import asyncio
from r_stats_mcp.server import server
print(len(asyncio.run(server.list_tools())), 'tools')
"

Adding a statistical tool means two edits: an R helper in src/r_stats_mcp/R/ returning list(md=, plots=), and a decorated function in server.py describing its parameters. The R files are sourced in filename order at worker startup.

src/r_stats_mcp/
├── server.py          MCP tool definitions and schemas
├── session.py         persistent R subprocess, transport, timeouts
└── R/
    ├── worker.R           protocol loop, plot capture
    ├── 00-util.R          markdown tables, formatting, session objects
    ├── 10-data.R          loading, inspection, reshaping
    ├── 20-descriptive.R   descriptives, frequencies, assumptions
    ├── 30-htest.R         t-tests, ANOVA, categorical, correlation
    ├── 40-regression.R    regression family, diagnostics, comparison
    ├── 50-psychometrics.R reliability, factor analysis, SEM, mediation
    ├── 60-survival-ts.R   survival analysis, time series
    └── 70-viz.R           ggplot2 charts

License

MIT — see LICENSE.

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