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Run R code via RStudio and support package development, testing, and jamovi builds.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "rstudio-mcp-server" yet — see the docs or source repo.
Connect to RStudio Desktop, inspect the current R package project, run devtools::check(), summarize errors and warnings, and suggest fixes.
A summary of package check results, a list of issues, and actionable fixes.
Run analysis.R in the current directory within RStudio, report key statistical results and generated chart file paths, and explain the main findings.
Execution results, key metrics, chart locations, and a brief analytical explanation.
Use the RStudio integration to inspect this jamovi module project configuration and run a build, then list failure reasons or output artifacts.
Build status, explanations of any errors, or generated artifacts and next-step publishing suggestions.
Connect to a live RStudio session to inspect data and run R code.
Run reproducible econometrics and statistical workflows through headless R execution.
Connect to Jupyter via MCP to run code and explore data interactively.
Expose Python functions from Jupyter Server as tools for MCP clients.
Connect AI agents to control local JupyterLab for coding and analysis.
Operate Redmine projects, issues, and workflows directly from MCP clients.