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servidor de rlang-mcp
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R-Server MCP
A specialized Model Context Protocol (MCP) server that enables AI models to generate data visualizations using R's ggplot2 library and execute R scripts.
Overview
This MCP server provides a streamlined interface for creating statistical visualizations and executing R scripts without requiring direct access to an R environment. It exposes two MCP tools:
-
render_ggplot
: Generates visualizations from R code containing ggplot2 commands -
execute_r_script
: Executes any R script and returns the text output
Features
- ggplot2 Rendering: Execute R code containing ggplot2 commands and return the resulting visualization
- R Script Execution: Execute any R script and return the text output
- Format Options: Support for PNG, JPEG, PDF, and SVG output formats
- Customization: Control image dimensions and resolution
- Error Handling: Clear error messages for invalid R code or rendering failures
- MCP Protocol Compliance: Full implementation of the Model Context Protocol
- Docker Integration: Secure execution of R code in isolated containers
Requirements
- Go 1.22 or later
- R 4.0 or later with ggplot2 package
- Docker (for containerized execution)
Building
# Build the Docker image
task docker:build
# Run the server in Docker
task docker:run
Using Docker with stdin/stdout
The server can be run in Docker while preserving stdin/stdout communication, which is essential for MCP:
# Build and run using docker-compose
./start_server.sh --docker
Or set an environment variable:
USE_DOCKER=true ./start_server.sh
This approach ensures that stdin and stdout are properly connected between the host and the container, allowing seamless MCP communication.
Usage
MCP Integration
To use this server with an MCP client, configure it in your MCP settings file:
Local Execution
{
"mcpServers": {
"r-server": {
"command": "/path/to/r-server",
"disabled": false,
"autoApprove": []
}
}
}
Docker Execution
{
"mcpServers": {
"r-server": {
"command": "/path/to/start_server.sh",
"args": ["--docker"],
"disabled": false,
"autoApprove": []
}
}
}
The MCP client will automatically communicate with the server using stdio transport, which is the recommended approach for stability and reliability. The dockerized version maintains this communication pattern while providing isolation and dependency management.
License
Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
See the LICENSE file for details.
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Reviews

user_txdEnm0Q
As a dedicated user of rlang-mcp-server, I am thoroughly impressed by its robust performance and seamless integration capabilities. Created by gdbelvin, this server stands out for its reliability and efficiency. Whether you're dealing with complex data analysis or straightforward tasks, this tool consistently delivers excellent results. Check it out at https://github.com/gdbelvin/rlang-mcp-server and see how it can enhance your workflow.