by matlab
The Simulink Agentic Toolkit gives your AI agent both the tools and the expertise to work effectively with Simulink and Model-Based Design.
# Add to your Claude Code skills
git clone https://github.com/matlab/simulink-agentic-toolkitGuides for using ai agents skills like simulink-agentic-toolkit.
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30 days in the Featured rail · terms & refunds
Give your AI coding agent the ability to read, build, edit, and test Simulink® models using Model-Based Design best practices.
The Simulink Agentic Toolkit packages MathWorks® Model-Based Design expertise for AI coding agents. It connects agents to Simulink through the Model Context Protocol (MCP), giving them both the ability (tools) and the knowledge (skills) to work with Simulink models effectively.
┌───────────┐ ┌───────────┐ ┌──────────┐
│ AI Agent │◄─MCP─►│MCP Server │◄─────►│ MATLAB / │
│ (Claude, │ │ (MATLAB │ │ Simulink │
│ Codex, │ │ MCP Core) │ └──────────┘
│ Copilot) │ └───────────┘
└───────────┘
▲
│ reads
┌─────┴─────┐
│ Skills │
│ (MBD best │
│ practices)│
└───────────┘
Your agent reads skills for domain knowledge, then calls MCP tools to interact with MATLAB and Simulink. The MATLAB MCP Core Server bridges the connection (downloaded during setup).
| Platform | Setup | Notes |
|---|---|---|
| Claude Code | Automated | |
| GitHub Copilot | Automated | |
| OpenAI Codex | Automated | |
| Gemini CLI | Automated | |
| Sourcegraph Amp | Automated |
Automated setup has been verified with basic workflows on each platform. The toolkit is under active development — please report issues if you encounter problems.
Full walkthrough: See the Getting Started guide for detailed instructions, platform-specific notes, verification steps, and troubleshooting.
Prerequisites:
The setupAgenticToolkit function handles installation, configuration, updates, and uninstallation for both the MATLAB and Simulink Agentic Toolkits. Download agenticToolkitInstaller.mltbx from the latest release, install it in MATLAB, then run:
setupAgenticToolkit("install")
This downloads the MCP server binary and toolkit files to ~/.matlab/agentic-toolkits/, then walks you through configuring your first coding agent (MCP server entry + skill registration). To set up additional agents later, run setupAgenticToolkit("configure"). To update to the latest version, run setupAgenticToolkit("update"). If your organization uses a CLI wrapper, pass AgentCLI="claude-code=/path/to/wrapper" during configure.
Existing users: If you previously set up the toolkit using the agent-driven workflow, you must uninstall that setup first. See Migrating from a Previous Installation in the Getting Started guide.
If you already have the MATLAB MCP Core Server installed or prefer full control, you can configure the toolkit manually. See Manual Setup in the Getting Started guide.
The MCP server connects to a running MATLAB session. Open MATLAB and run:
addpath("~/.matlab/agentic-toolkits/simulink")
satk_initialize
In MATLAB, open any Simulink model — your own, or a shipped example like f14:
openExample("simulink/AddBlockToModelFromLibraryExample") % only needed for R2023b+
open_system("f14")
Then ask your agent:
Describe the structure of the currently open model.
| Tool | What your agent can do |
|---|---|
model_overview |
Explore model architecture — see subsystem hierarchy, interfaces, and how major components connect |
model_read |
Understand model behavior — inspect blocks, algorithmic expressions, signal flow, and parameter values |
model_edit |
Build and modify models — add blocks, wire signals, create subsystems, and configure parameters |
model_check |
Validate model structure — detect unconnected ports, dangling lines, and Edit-Time Checks on States and Subcharts |
model_test |
Verify requirements — run human-readable Gherkin tests with automatic harness generation (requires Simulink Test) |
model_query_params |
Inspect any parameter — query block settings, signal properties, solver config, and logging flags |
model_resolve_params |
Get actual values — resolve workspace variables like Kp to their numeric values across all scopes |
Skills are organized in the skills catalog by product area.
Model-Based Design Core — core MBD skills for building, testing, and specifying Simulink models:
| Skill | What it teaches your agent |
|---|---|
building-simulink-models |
Best practices for structural model changes — adding blocks, wiring, layout |
filing-bug-reports |
Generate standalone bug reports for reproducing, investigating, and fixing issues |
managing-simulink-projects |
MATLAB project management — path setup, file registration, labels, model references, source control |
simulating-simulink-models |
Run simulations for data exploration, parameter sweeps, and custom analysis |
specifying-mbd-algorithms |
Specify algorithms for MBD — system specs, architecture specs, implementation and test plans |
specifying-plant-models |
How to specify plant models for closed-loop simulation |
testing-simulink-models |
How to test model behavior — reproduce issues, verify changes, regression tests |
generate-requirement-drafts |
Requirements generation — prefers Requirements Toolbox (.slreqx) with traceability links when available, falls back to structured YAML |
Model-Based System Engineering — MBSE skills for System Composer architecture models:
| Skill | What it teaches your agent |
|---|---|
building-architecture-models |
Build multi-layer system architecture models — components, interfaces, allocations, stereotypes, and requirements traceability (requires System Composer) |
simulink-agentic-toolkit/
├── skills-catalog/ # Agent skills (not auto-discovered)
│ ├── model-based-design-core/ # Core MBD skills (8 skills)
│ └── model-based-system-engineering/ # MBSE skills (1 skill)
├── tools/ # MCP tool implementations
├── satk_initialize.m # MATLAB session setup entry point
└── research-previews/ # Curated example tasks
The Agentic Task Explorer provides curated, multi-step tasks that demonstrate what agents can do with Simulink — model understanding, creation, modification, testing, bug fixing, and verification. Each task includes Simulink models and supporting files, ready to go.
slAgenticTaskExplorer
Select a task from the interactive UI. The explorer stages it into an isolated workspace with all required files, then opens your coding agent. Each task presents step-by-step prompts — copy each prompt into your coding agent and watch it work.
This is a research preview. Behavior and interfaces may change.
model_testbuilding-architecture-models skillbuilding-architecture-models skillThis toolkit relies on strong multi-step reasoning, tool use, and coding performance from the AI model.
We have tested the toolkit with higher-capability models, including Claude Opus and Sonnet, OpenAI GPT-5 models, and Gemini Pro models, and have generally seen good results on demanding workflows.
Model capability has a significant impact on quality. In our testing, lightweight or lower-capability models were less reliable for tasks such as model construction and complex edits, and were more likely to produce incomplete or incorrect results. These models may still be sufficient for simpler tasks, but for the best overall experience we recommend using a higher-capability model.
| Resource | Description |
|---|---|
| Getting Started | Setup tutorial with per-agent instructions and troubleshooting |
| Skills Catalog | Browse all agent skill groups and individual skills |
If you encounter a bug, use the filing-bug-reports skill to generate a report before opening a GitHub issue. Ask your agent:
File a bug report for this issue
The skill automatically captures environment details, reproduction steps, and error output — producing a complete report in your workspace. Then [