Tauri MCP Server

A Model Context Protocol (MCP) server for testing and interacting with Tauri v2 applications. This tool enables AI assistants to better understand, debug, and interact with Tauri apps during development.

The MCP protocol provides a standardized way for AI assistants to interact with external tools and systems. This server implements the MCP specification to expose Tauri application testing and debugging capabilities.

MCP Compliance

This server is fully compliant with the Model Context Protocol specification. It implements:

• ✅ JSON-RPC 2.0 transport over stdio
• ✅ Initialize/shutdown handshake
• ✅ Tools capability with 12 specialized Tauri testing tools
• ✅ Proper error handling with descriptive messages
• ✅ Tool schemas using JSON Schema format

For more details about MCP:

• MCP Introduction
• Building MCP Servers
• Tool Definitions

Features

Core Tools

• Process Management

  • launch_app - Launch Tauri applications with arguments
  • stop_app - Gracefully stop running apps
  • get_app_logs - Capture stdout/stderr output
  • monitor_resources - Track CPU, memory, and disk usage

• Window Manipulation

  • take_screenshot - Capture app window screenshots
  • get_window_info - Get window dimensions, position, and state

• Input Simulation

  • send_keyboard_input - Simulate keyboard input
  • send_mouse_click - Simulate mouse clicks
  • Mouse movement, dragging, and scrolling support

• Debugging Tools

  • execute_js - Execute JavaScript in the webview
  • get_devtools_info - Get DevTools connection info
  • WebDriver integration for advanced testing
  • Console log capture

• IPC Interaction

  • list_ipc_handlers - List registered Tauri commands
  • call_ipc_command - Call Tauri IPC commands
  • Event emission and listening

Installation

From Source

# Clone the repository
git clone https://github.com/dirvine/tauri-mcp.git
cd tauri-mcp

# Build and install
cargo install --path .

Using Cargo

cargo install tauri-mcp

Usage

As a Standalone Server

# Start the MCP server
tauri-mcp serve

# With custom host and port
tauri-mcp serve --host 127.0.0.1 --port 3000

# With a specific Tauri app
tauri-mcp --app-path ./my-tauri-app

Configuration

Create a tauri-mcp.toml file for configuration:

auto_discover = true
session_management = true
event_streaming = false
performance_profiling = false
network_interception = false

Environment Variables

• TAURI_MCP_LOG_LEVEL - Set log level (trace, debug, info, warn, error)
• TAURI_MCP_CONFIG - Path to config file (default: tauri-mcp.toml)

MCP Integration

Claude Desktop Configuration

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "tauri-mcp": {
      "command": "tauri-mcp",
      "args": ["serve"],
      "env": {
        "TAURI_MCP_LOG_LEVEL": "info"
      }
    }
  }
}

Available MCP Tools

All tools are exposed through the MCP protocol and can be called by AI assistants:

// Launch a Tauri app
await use_mcp_tool("tauri-mcp", "launch_app", {
  app_path: "/path/to/tauri-app",
  args: ["--debug"]
});

// Take a screenshot
await use_mcp_tool("tauri-mcp", "take_screenshot", {
  process_id: "uuid-here",
  output_path: "./screenshot.png"
});

// Execute JavaScript
await use_mcp_tool("tauri-mcp", "execute_js", {
  process_id: "uuid-here",
  javascript_code: "window.location.href"
});

// Send keyboard input
await use_mcp_tool("tauri-mcp", "send_keyboard_input", {
  process_id: "uuid-here",
  keys: "cmd+a"
});

Platform Support

• macOS - Full support including window management
• Windows - Full support with native window APIs
• Linux - X11 support (Wayland in progress)

Development

Building

# Debug build
cargo build

# Release build
cargo build --release

# Run tests
cargo test

Project Structure

tauri-mcp/
├── src/
│   ├── main.rs          # Entry point
│   ├── server.rs        # MCP server implementation
│   ├── tools/           # Tool implementations
│   │   ├── process.rs   # Process management
│   │   ├── window.rs    # Window manipulation
│   │   ├── input.rs     # Input simulation
│   │   ├── debug.rs     # Debugging tools
│   │   └── ipc.rs       # IPC interaction
│   └── utils/           # Utility modules
├── examples/            # Example Tauri apps
└── tests/              # Integration tests

Examples

Testing a Tauri App

# Launch the server
tauri-mcp serve

# In your AI assistant:
# 1. Launch the app
# 2. Take a screenshot
# 3. Send some input
# 4. Check the logs
# 5. Stop the app

Automated Testing Script

import asyncio
from mcp import Client

async def test_tauri_app():
    client = Client("tauri-mcp")
    
    # Launch app
    result = await client.call_tool("launch_app", {
        "app_path": "./my-app",
        "args": ["--test-mode"]
    })
    process_id = result["process_id"]
    
    # Wait for app to start
    await asyncio.sleep(2)
    
    # Take screenshot
    await client.call_tool("take_screenshot", {
        "process_id": process_id,
        "output_path": "./test-screenshot.png"
    })
    
    # Send input
    await client.call_tool("send_keyboard_input", {
        "process_id": process_id,
        "keys": "Hello, Tauri!"
    })
    
    # Get logs
    logs = await client.call_tool("get_app_logs", {
        "process_id": process_id,
        "lines": 50
    })
    print("App logs:", logs)
    
    # Stop app
    await client.call_tool("stop_app", {
        "process_id": process_id
    })

asyncio.run(test_tauri_app())

Troubleshooting

Common Issues

1. Permission Denied on macOS

   • Grant accessibility permissions in System Preferences
   • Required for input simulation

2. Screenshot Fails

   • Ensure screen recording permissions are granted
   • Check if the app window is visible

3. WebDriver Connection Failed

   • Ensure the Tauri app has DevTools enabled
   • Check if ChromeDriver is installed for WebDriver support

Debug Mode

Enable debug logging:

TAURI_MCP_LOG_LEVEL=debug tauri-mcp serve

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Implements the Model Context Protocol specification
• Designed for Tauri v2 applications
• Input simulation powered by enigo
• Screenshot functionality via screenshots-rs