MATLAB MCP Tool
A Model Context Protocol (MCP) server that provides tools for developing and running MATLAB files. Integrates with Claude Code, Cursor, and other MCP-compatible clients.
Prerequisites
- Python 3.10+
- MATLAB with Python Engine installed
- uv package manager (required)
Features
- Script Execution - Run complete scripts, individual sections (by index, title, or line range), maintain workspace context between executions, capture plots
- Workspace Management - Get full workspace, retrieve specific variables with field/depth/size control, inspect struct metadata, list and filter variables by name pattern or type
- Figure Analysis - Extract figure metadata (axes, labels, legends), get raw plot data, prepare figures for LLM-based analysis with custom prompts
- Code Quality - Lint MATLAB code via
checkcodewith severity filtering, supports inline code and file paths - Script Management - Create scripts, list sections with previews, read script content via MCP resource
Installation
Quick Start (Recommended)
One-command installation with auto-detection:
./install-matlab-mcp.sh
That's it! The installer will:
- ✅ Auto-detect MATLAB installations (including external volumes like
/Volumes/S1/) - ✅ Auto-install UV package manager if needed
- ✅ Create optimized virtual environment with MATLAB-compatible Python version
- ✅ Install all dependencies including MATLAB Python engine
- ✅ Generate MCP configuration ready for Cursor/Claude Code
- ✅ Verify installation works correctly
- ✅ Optionally configure Cursor automatically
Reduces installation time from 15+ minutes to ~2 minutes!
Advanced Installation
If you need custom configuration:
- Clone this repository:
git clone [repository-url]
cd matlab-mcp-tools
- Set custom MATLAB path (optional - installer auto-detects):
# Only needed if MATLAB is in unusual location
export MATLAB_PATH=/path/to/your/matlab/installation
- Run installer:
./install-matlab-mcp.sh
Legacy Installation (Manual)
Click to expand legacy manual installation steps
- Install uv package manager:
# Install uv using Homebrew
brew install uv
# OR install using pip
pip install uv
- Set MATLAB path environment variable:
# For macOS (auto-detection searches common locations)
export MATLAB_PATH=/Applications/MATLAB_R2024b.app
# For Windows (use Git Bash terminal)
export MATLAB_PATH="C:/Program Files/MATLAB/R2024b"
- Run legacy setup script:
./scripts/setup-matlab-mcp.sh
- Configure Cursor manually:
cp mcp-pip.json ~/.cursor/mcp.json
Testing Installation
Test your installation:
./scripts/test-matlab-mcp.sh
Installation complete! The MATLAB MCP server is now ready to use with Cursor/Claude Code.
Usage
- Start the MCP server:
matlab-mcp-server
This is equivalent to running:
python -m matlab_mcp.server
You should see a startup message confirming the server is running with 15 tools available.
- Configure your MCP client. For Claude Code, add to
.mcp.json:
{
"mcpServers": {
"matlab": {
"command": "/path/to/matlab-mcp-tools/.venv/bin/matlab-mcp-server",
"env": {
"MATLAB_PATH": "/Applications/MATLAB_R2024b.app"
}
}
}
}
For Cursor, use the auto-generated mcp-pip.json or add to ~/.cursor/mcp.json.
Hint: Find the MATLAB engine path with python -c "import matlab; print(matlab.__file__)".
- Available Tools (15):
| Category | Tool | Description |
|---|---|---|
| Scripts | execute_script |
Run MATLAB code or script file |
execute_section |
Execute by line range | |
execute_section_by_index |
Execute by section index (0-based) | |
execute_section_by_title |
Execute by section title (partial match) | |
get_script_sections |
List sections with titles and previews | |
create_matlab_script |
Create a new .m file | |
| Workspace | get_workspace |
Get all workspace variables |
get_variable |
Get specific variable (with field/depth/size control) | |
get_struct_info |
Get struct field metadata without data transfer | |
list_workspace_variables |
List/filter variables by name pattern or type | |
| Figures | get_figure_metadata |
Extract axes, labels, legends, subplot info |
get_plot_data |
Get raw x/y/z data from plot lines | |
analyze_figure |
Prepare figure image + metadata for LLM analysis | |
get_analysis_prompt |
Get/customize the figure analysis prompt | |
| Quality | matlab_lint |
Run checkcode on code or files |
Resource: matlab://scripts/{script_name} - Read script content
Examples
1. Simple Script Execution with Plot
This example demonstrates running a complete MATLAB script that generates a plot:
% test_plot.m
x = linspace(0, 2*pi, 100);
y = sin(x);
% Create a figure with some styling
figure;
plot(x, y, 'LineWidth', 2);
title('Sine Wave');
xlabel('x');
ylabel('sin(x)');
grid on;
% Add some annotations
text(pi, 0, '\leftarrow \pi', 'FontSize', 12);
To execute this script using the execute_script tool:
{
"script": "test_plot.m",
"is_file": true
}
The tool will execute the script and capture the generated plot, saving it to the output directory.
2. Section-Based Execution
This example shows how to execute specific sections of a MATLAB script:
%% Section 1: Data Generation
% Generate sample data
x = linspace(0, 10, 100);
y = sin(x);
fprintf('Generated %d data points\n', length(x));
%% Section 2: Basic Statistics
% Calculate basic statistics
mean_y = mean(y);
std_y = std(y);
max_y = max(y);
min_y = min(y);
fprintf('Statistics:\n');
fprintf('Mean: %.4f\n', mean_y);
fprintf('Std Dev: %.4f\n', std_y);
fprintf('Max: %.4f\n', max_y);
fprintf('Min: %.4f\n', min_y);
%% Section 3: Plotting
% Create visualization
figure('Position', [100, 100, 800, 400]);
subplot(1, 2, 1);
plot(x, y, 'b-', 'LineWidth', 2);
title('Signal');
xlabel('x');
ylabel('y');
grid on;
subplot(1, 2, 2);
histogram(y, 20);
title('Distribution');
xlabel('Value');
ylabel('Count');
grid on;
sgtitle('Signal Analysis');
To execute specific sections using execute_section_by_index:
{
"file_path": "section_test.m",
"section_index": 0
}
Or by title using execute_section_by_title:
{
"file_path": "section_test.m",
"section_title": "Data Generation"
}
The output will include:
Generated 100 data points
Statistics:
Mean: 0.0000
Std Dev: 0.7071
Max: 1.0000
Min: -1.0000
Output Directory
The tool creates matlab_output and test_output directories to store:
- Plot images generated during script execution
- Other temporary files
Error Handling
- Script execution errors are captured and returned with detailed error messages
- Workspace state is preserved even after errors
Installation Troubleshooting
The new install-matlab-mcp.sh installer handles most common issues automatically. If you encounter problems:
Common Issues and Solutions
1. MATLAB not found:
- The installer auto-detects MATLAB in common locations
- If you have MATLAB in unusual location:
export MATLAB_PATH=/your/matlab/path - Supported locations include external volumes (e.g.,
/Volumes/S1/Applications/)
2. UV package manager issues:
- The installer automatically installs UV if needed
- For manual installation:
curl -LsSf https://astral.sh/uv/install.sh | sh
3. Python version compatibility:
- Installer automatically selects MATLAB-compatible Python version
- MATLAB R2024b: Python 3.11, R2024a: Python 3.10, R2023x: Python 3.9
4. Permission errors:
- Run installer with appropriate permissions
- On Windows: use Git Bash with Admin privileges
5. Configuration issues:
- Use the auto-generated
mcp-pip.jsonconfiguration - Installer offers automatic Cursor configuration
Legacy Issues (if using manual installation)
Click for legacy troubleshooting
- Make sure
uvis installed before running legacy scripts - For ENONET errors, ensure Python executable consistency:
{
"command": "bash",
"args": ["-c", "source ~/.zshrc && /path/to/matlab-mcp-install/.venv/bin/matlab-mcp-server"]
}
- MATLAB Python Engine compatibility: See MATLAB Engine docs
Still Having Issues?
- Check installer output for specific error messages
- Verify MATLAB license is valid and active
- Test manually:
.venv/bin/matlab-mcp-server --help - Open an issue with installer output if problem persists
Contributing
- Fork the repository
- Create a feature branch
- Submit a pull request
License
This project is licensed under the BSD-3-Clause License. See the LICENSE file for details.