# MCP Server for Home Assistant 🏠🤖
[](LICENSE) [](https://bun.sh) [](https://www.typescriptlang.org) [](https://smithery.ai/server/@jango-blockchained/advanced-homeassistant-mcp)
## Overview 🌐
MCP (Model Context Protocol) Server is my lightweight integration tool for Home Assistant, providing a flexible interface for device management and automation. It's designed to be fast, secure, and easy to use. Built with Bun for maximum performance.
## Why Bun? 🚀
I chose Bun as the runtime for several key benefits:
- ⚡ **Blazing Fast Performance**
- Up to 4x faster than Node.js
- Built-in TypeScript support
- Optimized file system operations
- 🎯 **All-in-One Solution**
- Package manager (faster than npm/yarn)
- Bundler (no webpack needed)
- Test runner (built-in testing)
- TypeScript transpiler
- 🔋 **Built-in Features**
- SQLite3 driver
- .env file loading
- WebSocket client/server
- File watcher
- Test runner
- 💾 **Resource Efficient**
- Lower memory usage
- Faster cold starts
- Better CPU utilization
- 🔄 **Node.js Compatibility**
- Runs most npm packages
- Compatible with Express/Fastify
- Native Node.js APIs
## Core Features ✨
- 🔌 Basic device control via REST API
- 📡 WebSocket/Server-Sent Events (SSE) for state updates
- 🤖 Simple automation rule management
- 🔐 JWT-based authentication
- 🎤 Optional speech features:
- 🗣️ Wake word detection ("hey jarvis", "ok google", "alexa")
- 🎯 Speech-to-text using fast-whisper
- 🌍 Multiple language support
- 🚀 GPU acceleration support
## System Architecture 📊
```mermaid
flowchart TB
subgraph Client["Client Applications"]
direction TB
Web["Web Interface"]
Mobile["Mobile Apps"]
Voice["Voice Control"]
end
subgraph MCP["MCP Server"]
direction TB
API["REST API"]
WS["WebSocket/SSE"]
Auth["Authentication"]
subgraph Speech["Speech Processing (Optional)"]
direction TB
Wake["Wake Word Detection"]
STT["Speech-to-Text"]
subgraph STT_Options["STT Options"]
direction LR
Whisper["Whisper"]
FastWhisper["Fast Whisper"]
end
Wake --> STT
STT --> STT_Options
end
end
subgraph HA["Home Assistant"]
direction TB
HASS_API["HASS API"]
HASS_WS["HASS WebSocket"]
Devices["Smart Devices"]
end
Client --> MCP
MCP --> HA
HA --> Devices
style Speech fill:#f9f,stroke:#333,stroke-width:2px
style STT_Options fill:#bbf,stroke:#333,stroke-width:1px
```
## Prerequisites 📋
- 🚀 [Bun runtime](https://bun.sh) (v1.0.26+)
- 🏡 [Home Assistant](https://www.home-assistant.io/) instance
- 🐳 Docker (optional, recommended for deployment)
- 🖥️ Node.js 18+ (optional, for speech features)
- 🎮 NVIDIA GPU with CUDA support (optional, for faster speech processing)
## Quick Start 🚀
1. Clone my repository:
```bash
git clone https://github.com/jango-blockchained/homeassistant-mcp.git
cd homeassistant-mcp
```
2. Set up the environment:
```bash
# Make my setup script executable
chmod +x scripts/setup-env.sh
# Run setup (defaults to development)
./scripts/setup-env.sh
# Or specify an environment:
NODE_ENV=production ./scripts/setup-env.sh
# Force override existing files:
./scripts/setup-env.sh --force
```
3. Configure your settings:
- Edit `.env` file with your Home Assistant details
- Required: Add your `HASS_TOKEN` (long-lived access token)
4. Build and launch with Docker:
```bash
# Build options:
# Standard build
./docker-build.sh
# Build with speech support
./docker-build.sh --speech
# Build with speech and GPU support
./docker-build.sh --speech --gpu
# Launch:
docker compose up -d
# With speech features:
docker compose -f docker-compose.yml -f docker-compose.speech.yml up -d
```
## Docker Build Options 🐳
My Docker build script (`docker-build.sh`) supports different configurations:
### 1. Standard Build
```bash
./docker-build.sh
```
- Basic MCP server functionality
- REST API and WebSocket support
- No speech features
### 2. Speech-Enabled Build
```bash
./docker-build.sh --speech
```
- Includes wake word detection
- Speech-to-text capabilities
- Pulls required images:
- `onerahmet/openai-whisper-asr-webservice`
- `rhasspy/wyoming-openwakeword`
### 3. GPU-Accelerated Build
```bash
./docker-build.sh --speech --gpu
```
- All speech features
- CUDA GPU acceleration
- Optimized for faster processing
- Float16 compute type for better performance
### Build Features
- 🔄 Automatic resource allocation
- 💾 Memory-aware building
- 📊 CPU quota management
- 🧹 Automatic cleanup
- 📝 Detailed build logs
- 📊 Build summary and status
## Environment Configuration 🔧
I've implemented a hierarchical configuration system:
### File Structure 📁
1. `.env.example` - My template with all options
2. `.env` - Your configuration (copy from .env.example)
3. Environment overrides:
- `.env.dev` - Development settings
- `.env.prod` - Production settings
- `.env.test` - Test settings
### Loading Priority ⚡
Files load in this order:
1. `.env` (base config)
2. Environment-specific file:
- `NODE_ENV=development` → `.env.dev`
- `NODE_ENV=production` → `.env.prod`
- `NODE_ENV=test` → `.env.test`
Later files override earlier ones.
## Speech Features Setup 🎤
### Prerequisites
1. 🐳 Docker installed and running
2. 🎮 NVIDIA GPU with CUDA (optional)
3. 💾 4GB+ RAM (8GB+ recommended)
### Configuration
1. Enable speech in `.env`:
```bash
ENABLE_SPEECH_FEATURES=true
ENABLE_WAKE_WORD=true
ENABLE_SPEECH_TO_TEXT=true
WHISPER_MODEL_PATH=/models
WHISPER_MODEL_TYPE=base
```
2. Choose your STT engine:
```bash
# For standard Whisper
STT_ENGINE=whisper
# For Fast Whisper (GPU recommended)
STT_ENGINE=fast-whisper
CUDA_VISIBLE_DEVICES=0 # Set GPU device
```
### Available Models 🤖
Choose based on your needs:
- `tiny.en`: Fastest, basic accuracy
- `base.en`: Good balance (recommended)
- `small.en`: Better accuracy, slower
- `medium.en`: High accuracy, resource intensive
- `large-v2`: Best accuracy, very resource intensive
## Development 💻
```bash
# Install dependencies
bun install
# Run in development mode
bun run dev
# Run tests
bun test
# Run with hot reload
bun --hot run dev
# Build for production
bun build ./src/index.ts --target=bun
# Run production build
bun run start
```
### Performance Comparison 📊
| Operation | Bun | Node.js |
|-----------|-----|---------|
| Install Dependencies | ~2s | ~15s |
| Cold Start | 300ms | 1000ms |
| Build Time | 150ms | 4000ms |
| Memory Usage | ~150MB | ~400MB |
## Documentation 📚
### Core Documentation
- [Configuration Guide](docs/configuration.md)
- [API Documentation](docs/api.md)
- [Troubleshooting](docs/troubleshooting.md)
### Advanced Features
- [Natural Language Processing](docs/nlp.md) - AI-powered automation analysis and control
- [Custom Prompts Guide](docs/prompts.md) - Create and customize AI behavior
- [Extras & Tools](docs/extras.md) - Additional utilities and advanced features
### Extra Tools 🛠️
I've included several powerful tools in the `extra/` directory to enhance your Home Assistant experience:
1. **Home Assistant Analyzer CLI** (`ha-analyzer-cli.ts`)
- Deep automation analysis using AI models
- Security vulnerability scanning
- Performance optimization suggestions
- System health metrics
2. **Speech-to-Text Example** (`speech-to-text-example.ts`)
- Wake word detection
- Speech-to-text transcription
- Multiple language support
- GPU acceleration support
3. **Claude Desktop Setup** (`claude-desktop-macos-setup.sh`)
- Automated Claude Desktop installation for macOS
- Environment configuration
- MCP integration setup
See [Extras Documentation](docs/extras.md) for detailed usage instructions and examples.
## Client Integration 🔗
### Cursor Integration 🖱️
Add to `.cursor/config/config.json`:
```json
{
"mcpServers": {
"homeassistant-mcp": {
"command": "bun",
"args": ["run", "start"],
"cwd": "${workspaceRoot}",
"env": {
"NODE_ENV": "development"
}
}
}
}
```
### Claude Desktop 💬
Add to your Claude config:
```json
{
"mcpServers": {
"homeassistant-mcp": {
"command": "bun",
"args": ["run", "start", "--port", "8080"],
"env": {
"NODE_ENV": "production"
}
}
}
}
```
### Command Line 💻
Windows users can use the provided script:
1. Go to `scripts` directory
2. Run `start_mcp.cmd`
## License 📄
MIT License. See [LICENSE](LICENSE) for details.
## Author 👨💻
Created by [jango-blockchained](https://github.com/jango-blockchained)