Matlab Agentic Toolkit

MATLAB Agentic Toolkit

Skill by ara.so — AI Agent Skills collection.

The MATLAB Agentic Toolkit enables AI coding agents to work with MATLAB by providing the Model Context Protocol (MCP) server and curated skills. This toolkit gives agents the knowledge and context to write idiomatic MATLAB code, run tests, diagnose errors, and follow best practices without hallucinating functions or missing features.

What It Does

• MCP Server Integration: Automatically installs and configures the MATLAB MCP Core Server to connect your AI agent to MATLAB
• Curated Skills: Provides domain-specific expertise across automotive, signal processing, computer vision, robotics, and more
• Code Execution: Run MATLAB code, execute tests, perform static analysis, and build apps through MCP tools
• Best Practices: Guides agents through MATLAB workflows, conventions, and idiomatic patterns

Installation

Prerequisites

• MATLAB R2020b or later
• Git
• AI coding agent (Claude Code, GitHub Copilot, OpenAI Codex, Gemini CLI, or Sourcegraph Amp)

Basic Installation

Clone the repository:

git clone https://github.com/matlab/matlab-agentic-toolkit.git
cd matlab-agentic-toolkit

Ask your AI agent to set up the toolkit:

Set up the MATLAB Agentic Toolkit

The agent will:

1. Locate your MATLAB installation(s)
2. Download the MATLAB MCP Core Server
3. Write global agent configuration
4. Register all skills

Start a new session after setup completes.

Skills-Only Installation (Claude Code)

If you already have the MATLAB MCP Core Server installed:

claude plugin marketplace add "https://github.com/matlab/matlab-agentic-toolkit"
claude plugin install matlab-core@matlab-agentic-toolkit

This adds skills without modifying your existing MCP configuration.

Alternative MATLAB-Based Installer

For users who want both MATLAB and Simulink toolkits or need advanced configuration:

1. Download agenticToolkitInstaller.mltbx from the Simulink Agentic Toolkit releases
2. Open the file in MATLAB to install
3. Run in MATLAB:

setupAgenticToolkit

This installer supports:

• Installing both MATLAB and Simulink toolkits
• Connecting to existing MATLAB sessions (--matlab-session-mode=existing)
• Project-specific configuration (not just global)
• Zero token consumption during setup

MCP Tools

After installation, your agent can use these MCP tools to interact with MATLAB:

evaluate_matlab_code

Execute MATLAB code and return command window output:

Run this MATLAB code: x = linspace(0, 2*pi, 100); y = sin(x); plot(x, y);

The agent will use evaluate_matlab_code to execute and return results.

run_matlab_file

Execute a MATLAB script or function file:

Run the file analyze_data.m with the current data

run_matlab_test_file

Execute MATLAB tests via runtests with structured results:

Run tests in test_signal_processing.m and show the results

check_matlab_code

Perform static analysis using Code Analyzer:

Check this MATLAB code for issues: function out = calc(x) out = x^2 end

detect_matlab_toolboxes

List installed MATLAB version and available toolboxes:

What version of MATLAB is running? List the installed toolboxes.

MCP Resources

The server provides these resources for context:

• matlab_coding_guidelines: MATLAB coding standards and best practices
• plain_text_live_code_guidelines: Format rules for MATLAB Live Scripts

Available Skills

Skills provide domain-specific expertise to your agent. The toolkit includes:

Core Skills

MATLAB Core — Foundational MATLAB programming, syntax, and workflows

• Array operations and indexing
• Function definitions and anonymous functions
• Control flow and error handling
• Workspace management

MATLAB Data Import and Analysis — Data loading, preprocessing, and analysis

• Reading from files (CSV, Excel, JSON, images)
• Data cleaning and transformation
• Statistical analysis and visualization
• Table and timetable operations

MATLAB Software Development — Professional development practices

• Object-oriented programming
• Unit testing with matlab.unittest
• Code organization and packaging
• Version control integration

MATLAB App Building — Creating interactive applications

• App Designer workflows
• UI components and callbacks
• Deployment options

Domain-Specific Skills

Automotive — Automotive engineering workflows

• CAN/LIN communication
• Vehicle modeling
• Calibration and testing

Computational Biology — Bioinformatics and systems biology

• Sequence analysis
• Pathway modeling
• Microarray data analysis

Image Processing and Computer Vision — Image analysis and vision algorithms

• Image filtering and enhancement
• Object detection and tracking
• Camera calibration
• Deep learning for vision

Robotics and Autonomous Systems — Robotics development

• ROS integration
• Path planning
• Sensor fusion
• Simulation with Gazebo

Signal Processing — Digital signal analysis

• Filter design
• Spectral analysis
• Wavelet transforms
• Time-frequency analysis

Wireless Communications — Communication system design

• Modulation and coding
• MIMO systems
• 5G NR workflows
• Channel modeling

RF and Mixed Signal — RF circuit design and analysis

• S-parameter analysis
• Amplifier design
• PLL design

Test and Measurement — Instrument control and data acquisition

• Instrument communication (VISA, GPIB)
• Data acquisition workflows
• Automated testing

Reporting and Database Access — Report generation and database connectivity

• MATLAB Report Generator
• Database Toolbox workflows
• Custom report templates

Common Usage Patterns

Running MATLAB Code

Ask your agent to execute code directly:

Create a matrix A with random values and compute its eigenvalues

Agent uses evaluate_matlab_code:

A = rand(5, 5);
eigenvalues = eig(A);
disp(eigenvalues);

Creating and Testing Functions

Write a function to calculate the moving average of a signal, 
then create tests for it

Agent creates movingAverage.m:

function y = movingAverage(x, windowSize)
    % MOVINGAVERAGE Compute moving average of input signal
    %   y = movingAverage(x, windowSize) returns the moving average
    %   of x using a window of size windowSize
    
    arguments
        x (:,1) double
        windowSize (1,1) double {mustBePositive, mustBeInteger}
    end
    
    b = ones(1, windowSize) / windowSize;
    y = filter(b, 1, x);
end

And test file test_movingAverage.m:

classdef test_movingAverage < matlab.unittest.TestCase
    
    methods(Test)
        function testBasicOperation(testCase)
            x = [1 2 3 4 5]';
            y = movingAverage(x, 3);
            testCase.verifyEqual(length(y), length(x));
        end
        
        function testConstantSignal(testCase)
            x = ones(10, 1) * 5;
            y = movingAverage(x, 3);
            % After initial transient, output should equal input
            testCase.verifyEqual(y(3:end), x(3:end), 'AbsTol', 1e-10);
        end
    end
end

Then runs tests: run_matlab_test_file('test_movingAverage.m')

Data Analysis Workflow

Load data.csv, clean it, and create a visualization showing trends over time

Agent creates complete workflow:

% Import data
data = readtable('data.csv');

% Clean data - remove rows with missing values
cleanData = rmmissing(data);

% Convert to timetable if date column exists
if ismember('Date', cleanData.Properties.VariableNames)
    cleanData.Date = datetime(cleanData.Date);
    tt = table2timetable(cleanData);
    
    % Plot time series
    figure;
    stackedplot(tt);
    title('Data Trends Over Time');
else
    % Create standard plot
    figure;
    plot(cleanData.Variables);
    legend(cleanData.Properties.VariableNames);
    title('Data Trends');
end

Building a Simple App

Create an app that allows users to upload an image and apply filters

Agent creates App Designer code or programmatic UI:

function imageFilterApp
    % Create figure and components
    fig = uifigure('Name', 'Image Filter App');
    g = uigridlayout(fig, [3 2]);
    
    % Load button
    btnLoad = uibutton(g, 'Text', 'Load Image', ...
        'ButtonPushedFcn', @loadImage);
    btnLoad.Layout.Row = 1;
    btnLoad.Layout.Column = 1;
    
    % Filter dropdown
    dd = uidropdown(g, 'Items', {'None', 'Gaussian', 'Median', 'Edge'});
    dd.Layout.Row = 1;
    dd.Layout.Column = 2;
    dd.ValueChangedFcn = @applyFilter;
    
    % Image axes
    ax = uiaxes(g);
    ax.Layout.Row = [2 3];
    ax.Layout.Column = [1 2];
    
    img = [];
    
    function loadImage(~, ~)
        [file, path] = uigetfile({'*.jpg;*.png;*.bmp', 'Image Files'});
        if file
            img = imread(fullfile(path, file));
            imshow(img, 'Parent', ax);
        end
    end
    
    function applyFilter(~, ~)
        if isempty(img)
            return;
        end
        
        filtered = img;
        switch dd.Value
            case 'Gaussian'
                filtered = imgaussfilt(img, 2);
            case 'Median'
                filtered = medfilt3(img);
            case 'Edge'
                if size(img, 3) == 3
                    gray = rgb2gray(img);
                else
                    gray = img;
                end
                filtered = edge(gray, 'Canny');
        end
        
        imshow(filtered, 'Parent', ax);
    end
end

Configuration

Global vs. Project Configuration

By default, the toolkit configures your agent globally. To use project-specific configuration with the MATLAB-based installer:

setupAgenticToolkit('Scope', 'project')

Connecting to Existing MATLAB Session

To connect the MCP server to an already-running MATLAB session:

setupAgenticToolkit('SessionMode', 'existing')

Or manually configure the MCP server with --matlab-session-mode=existing flag.

Verifying Installation

Ask your agent:

What version of MATLAB is running? List the installed toolboxes.

Expected output shows MATLAB version and all installed toolboxes.

Troubleshooting

MCP Server Not Responding

1. Check MATLAB is installed and accessible:

matlab -batch "disp(version)"

2. Verify MCP server configuration in your agent's config file
3. Restart your agent session
4. Check MCP server logs (location varies by agent)

Skills Not Available

If skills aren't loading:

1. Verify installation completed successfully
2. For Claude Code, check plugins:

claude plugin list

3. Reinstall skills-only if needed
4. Start a new agent session

Code Execution Errors

When evaluate_matlab_code fails:

1. Ask agent to use check_matlab_code first for syntax validation
2. Verify MATLAB path includes required toolboxes
3. Check for workspace variable conflicts
4. Review MATLAB command window output for detailed errors

Permission Issues

On Windows, if installation fails with permission errors:

• Run your terminal/agent as administrator
• Ensure MATLAB installation directory is accessible

On macOS/Linux:

• Check file permissions: ls -la ~/.config/ (or agent-specific config location)
• Ensure MATLAB can be executed: which matlab

Toolbox Detection Issues

If detect_matlab_toolboxes returns incomplete results:

% Run in MATLAB directly to verify
ver

Compare output with agent results. Missing toolboxes may need licensing or installation.

Security Considerations

Always review tool calls before execution. The MCP server executes code with full MATLAB privileges:

1. Validate all evaluate_matlab_code calls before approval
2. Review file operations (run_matlab_file, save, load)
3. Check network operations (database connections, web requests)
4. Keep humans in the loop for critical operations
5. Use read-only mode when analyzing untrusted code

Licensing: MCP servers must comply with MathWorks Software License Agreement and cannot be shared by multiple users. Contact MathWorks for multi-user deployments.

Environment Variables

The toolkit respects these environment variables:

• MATLAB_ROOT: Override MATLAB installation path detection
• MCP_SERVER_LOG_LEVEL: Set logging verbosity (debug, info, warn, error)

Reference in configuration:

export MATLAB_ROOT=/Applications/MATLAB_R2024b.app

Further Resources

• Configuration and Troubleshooting Guide
• Skills Catalog
• MATLAB MCP Core Server
• MCP Specification
• MathWorks Agentic AI Campaign