A Model Context Protocol (MCP) server that provides tools for developing, running, and debugging MATLAB files. This tool integrates with Cline and other MCP-compatible clients to provide interactive MATLAB development capabilities.

• Python 3.8+
• MATLAB with Python Engine installed
• MCP SDK for Python (pip install modelcontextprotocol)
• MATLAB Engine for Python (pip install matlabengine)

1. Execute MATLAB Scripts

   • Run complete MATLAB scripts
   • Execute individual script sections
   • Maintain workspace context between executions
   • Capture and display plots

2. Interactive Debugging

   • Set breakpoints in MATLAB scripts
   • Debug script execution
   • Inspect workspace variables

3. Section-based Execution

   • Execute specific sections of MATLAB files
   • Support for cell mode (%% delimited sections)
   • Maintain workspace context between sections

1. Clone this repository:

git clone [repository-url]
cd matlab-mcp-tool

2. Create and activate a conda environment:

conda create -n matlab-mcp python=3.8
conda activate matlab-mcp

3. Install dependencies:

pip install modelcontextprotocol matlabengine

1. Start the MCP server:

python src/matlab_bridge.py

2. Configure Cline to use the MATLAB MCP server by adding to your Cline configuration:

{
  "mcpServers": {
    "matlab": {
      "command": "python",
      "args": ["/path/to/matlab-mcp-tool/src/matlab_bridge.py"],
      "env": {
        "PYTHONPATH": "/path/to/matlab/engine/installation"
      }
    }
  }
}

3. Available Tools:

• execute_matlab_script

  {
    "script": "x = 1:10;\nplot(x, x.^2);",
    "isFile": false
  }

• execute_matlab_section

  {
    "filePath": "analysis.m",
    "sectionStart": 1,
    "sectionEnd": 10
  }

• debug_matlab_script

  {
    "script": "debug_script.m",
    "breakpoints": [5, 10, 15]
  }

The tool creates an output directory in the current working directory to store:

• Plot images generated during script execution
• Debug output files
• Other temporary files

• Script execution errors are captured and returned with detailed error messages
• Workspace state is preserved even after errors
• Debug sessions are properly cleaned up

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

BSD-3-Clause