chore: refine configuration and setup scripts for improved usability

- Update README with minor text formatting - Improve Smithery configuration command formatting - Enhance macOS setup script with WebSocket URL conversion and security hardening
docs: add MCP client integration documentation and scripts
2025-02-05 02:20:08 +01:00 · 2025-02-05 00:48:45 +01:00 · 2025-02-04 22:51:11 +01:00 · 2025-02-04 22:51:06 +01:00 · 2025-02-04 21:42:50 +01:00 · 2025-02-04 20:29:52 +00:00
16 changed files with 1235 additions and 128 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -73,7 +73,6 @@ temp/
 .cloud/
 *.db
 *.db-* 
-bun.lockb
 .cursor/
 .cursor*
 .cursorconfig
--- a/72
+++ b/72
@@ -1,36 +1,70 @@
-# Use Bun as the base image
-FROM oven/bun:1.0.25 as builder
+# Use Node.js as base for building
+FROM node:20-slim as builder

 # Set working directory
 WORKDIR /app

-# Copy only package files first for better layer caching
-COPY package.json bun.lockb ./
+# Install bun
+RUN npm install -g bun@1.0.25

-# Install dependencies with production flag
-RUN bun install --frozen-lockfile --production
+# Install only the minimal dependencies needed and clean up in the same layer
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ca-certificates \
+    curl \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean \
+    && rm -rf /var/cache/apt/*

-# Copy source code
-COPY . .
+# Set build-time environment variables
+ENV NODE_ENV=production \
+    NODE_OPTIONS="--max-old-space-size=2048" \
+    BUN_INSTALL_CACHE=0

-# Build TypeScript
-RUN bun run build
+# Copy only package files first
+COPY package.json ./

-# Create production image
-FROM oven/bun:1.0.25-slim
+# Install dependencies with a clean slate
+RUN rm -rf node_modules .bun bun.lockb && \
+    bun install --no-save
+
+# Copy source files and build
+COPY src ./src
+COPY tsconfig*.json ./
+RUN bun build ./src/index.ts --target=bun --minify --outdir=./dist
+
+# Create a smaller production image
+FROM node:20-slim as runner
+
+# Install bun in production image
+RUN npm install -g bun@1.0.25
+
+# Set production environment variables
+ENV NODE_ENV=production \
+    NODE_OPTIONS="--max-old-space-size=1024"
+
+# Create a non-root user
+RUN addgroup --system --gid 1001 nodejs && \
+    adduser --system --uid 1001 bunjs

 WORKDIR /app

 # Copy only the necessary files from builder
-COPY --from=builder /app/dist ./dist
-COPY --from=builder /app/node_modules ./node_modules
-COPY package.json ./
+COPY --from=builder --chown=bunjs:nodejs /app/dist ./dist
+COPY --from=builder --chown=bunjs:nodejs /app/node_modules ./node_modules
+COPY --chown=bunjs:nodejs package.json ./

-# Set production environment
-ENV NODE_ENV=production
+# Create logs directory with proper permissions
+RUN mkdir -p /app/logs && chown -R bunjs:nodejs /app/logs
+
+# Switch to non-root user
+USER bunjs
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:4000/health || exit 1

 # Expose port
 EXPOSE 4000

-# Start the application
-CMD ["bun", "run", "start"] 
+# Start the application with optimized flags
+CMD ["bun", "--smol", "run", "start"] 
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@

 Welcome to the **Model Context Protocol (MCP) Server for Home Assistant**! This robust platform bridges Home Assistant with cutting-edge Language Learning Models (LLMs), enabling natural language interactions and real-time automation of your smart devices. Imagine entering your home, saying:  

-> “Hey MCP, dim the lights and start my evening playlist,”  
+> "Hey MCP, dim the lights and start my evening playlist,"  

 and watching your home transform instantly—that's the magic that MCP Server delivers!

@@ -122,6 +122,14 @@ For further technical details, check out our [Documentation Index](docs/index.md

 ## Installation 🛠

+### Installing via Smithery
+
+To install Home Assistant MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@jango-blockchained/advanced-homeassistant-mcp):
+
+```bash
+npx -y @smithery/cli install @jango-blockchained/advanced-homeassistant-mcp --client claude
+```
+
 ### 🐳 Docker Setup (Recommended)

 For a hassle-free, containerized deployment:
@@ -285,4 +293,80 @@ This project is licensed under the MIT License. See [LICENSE](LICENSE) for full

 ---

-🔋 Batteries included.
+🔋 Batteries included.
+
+## MCP Client Integration
+
+This MCP server can be integrated with various clients that support the Model Context Protocol. Below are instructions for different client integrations:
+
+### Cursor Integration
+
+The server can be integrated with Cursor by adding the configuration to `.cursor/config/config.json`:
+
+```json
+{
+  "mcpServers": {
+    "homeassistant-mcp": {
+      "command": "bun",
+      "args": ["run", "start"],
+      "cwd": "${workspaceRoot}",
+      "env": {
+        "NODE_ENV": "development"
+      }
+    }
+  }
+}
+```
+
+### Claude Desktop Integration
+
+For Claude Desktop, add the following to your Claude configuration file:
+
+```json
+{
+  "mcpServers": {
+    "homeassistant-mcp": {
+      "command": "bun",
+      "args": ["run", "start", "--port", "8080"],
+      "env": {
+        "NODE_ENV": "production"
+      }
+    }
+  }
+}
+```
+
+### Cline Integration
+
+For Cline-based clients, add the following configuration:
+
+```json
+{
+  "mcpServers": {
+    "homeassistant-mcp": {
+      "command": "bun",
+      "args": [
+        "run",
+        "start",
+        "--enable-cline",
+        "--config",
+        "${configDir}/.env"
+      ],
+      "env": {
+        "NODE_ENV": "production",
+        "CLINE_MODE": "true"
+      }
+    }
+  }
+}
+```
+
+### Command Line Usage
+
+#### Windows
+A CMD script is provided in the `scripts` directory. To use it:
+
+1. Navigate to the `scripts` directory
+2. Run `start_mcp.cmd`
+
+The script will start the MCP server with default configuration.
--- a/bun.lock
+++ b/bun.lock
@@ -0,0 +1,570 @@
+{
+  "lockfileVersion": 0,
+  "workspaces": {
+    "": {
+      "dependencies": {
+        "@elysiajs/cors": "^1.2.0",
+        "@elysiajs/swagger": "^1.2.0",
+        "@types/jsonwebtoken": "^9.0.5",
+        "@types/node": "^20.11.24",
+        "@types/sanitize-html": "^2.9.5",
+        "@types/ws": "^8.5.10",
+        "dotenv": "^16.4.5",
+        "elysia": "^1.2.11",
+        "helmet": "^7.1.0",
+        "jsonwebtoken": "^9.0.2",
+        "node-fetch": "^3.3.2",
+        "sanitize-html": "^2.11.0",
+        "typescript": "^5.3.3",
+        "winston": "^3.11.0",
+        "winston-daily-rotate-file": "^5.0.0",
+        "ws": "^8.16.0",
+        "zod": "^3.22.4",
+      },
+      "devDependencies": {
+        "@types/uuid": "^10.0.0",
+        "@typescript-eslint/eslint-plugin": "^7.1.0",
+        "@typescript-eslint/parser": "^7.1.0",
+        "bun-types": "^1.2.2",
+        "eslint": "^8.57.0",
+        "eslint-config-prettier": "^9.1.0",
+        "eslint-plugin-prettier": "^5.1.3",
+        "husky": "^9.0.11",
+        "prettier": "^3.2.5",
+        "supertest": "^6.3.3",
+        "uuid": "^11.0.5",
+      },
+    },
+  },
+  "packages": {
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+    "winston": ["winston@3.17.0", "", { "dependencies": { "@colors/colors": "^1.6.0", "@dabh/diagnostics": "^2.0.2", "async": "^3.2.3", "is-stream": "^2.0.0", "logform": "^2.7.0", "one-time": "^1.0.0", "readable-stream": "^3.4.0", "safe-stable-stringify": "^2.3.1", "stack-trace": "0.0.x", "triple-beam": "^1.3.0", "winston-transport": "^4.9.0" } }, "sha512-DLiFIXYC5fMPxaRg832S6F5mJYvePtmO5G9v9IgUFPhXm9/GkXarH/TUrBAVzhTCzAj9anE/+GjrgXp/54nOgw=="],
+
+    "winston-daily-rotate-file": ["winston-daily-rotate-file@5.0.0", "", { "dependencies": { "file-stream-rotator": "^0.6.1", "object-hash": "^3.0.0", "triple-beam": "^1.4.1", "winston-transport": "^4.7.0" }, "peerDependencies": { "winston": "^3" } }, "sha512-JDjiXXkM5qvwY06733vf09I2wnMXpZEhxEVOSPenZMii+g7pcDcTBt2MRugnoi8BwVSuCT2jfRXBUy+n1Zz/Yw=="],
+
+    "winston-transport": ["winston-transport@4.9.0", "", { "dependencies": { "logform": "^2.7.0", "readable-stream": "^3.6.2", "triple-beam": "^1.3.0" } }, "sha512-8drMJ4rkgaPo1Me4zD/3WLfI/zPdA9o2IipKODunnGDcuqbHwjsbB79ylv04LCGGzU0xQ6vTznOMpQGaLhhm6A=="],
+
+    "word-wrap": ["word-wrap@1.2.5", "", {}, "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA=="],
+
+    "wrappy": ["wrappy@1.0.2", "", {}, "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="],
+
+    "ws": ["ws@8.18.0", "", { "peerDependencies": { "bufferutil": "^4.0.1", "utf-8-validate": ">=5.0.2" }, "optionalPeers": ["bufferutil", "utf-8-validate"] }, "sha512-8VbfWfHLbbwu3+N6OKsOMpBdT4kXPDDB9cJk2bJ6mh9ucxdlnNvH1e+roYkKmN9Nxw2yjz7VzeO9oOz2zJ04Pw=="],
+
+    "yocto-queue": ["yocto-queue@0.1.0", "", {}, "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q=="],
+
+    "zhead": ["zhead@2.2.4", "", {}, "sha512-8F0OI5dpWIA5IGG5NHUg9staDwz/ZPxZtvGVf01j7vHqSyZ0raHY+78atOVxRqb73AotX22uV1pXt3gYSstGag=="],
+
+    "zod": ["zod@3.24.1", "", {}, "sha512-muH7gBL9sI1nciMZV67X5fTKKBLtwpZ5VBp1vsOQzj1MhrBZ4wlVCm3gedKZWLp0Oyel8sIGfeiz54Su+OVT+A=="],
+
+    "@scalar/themes/@scalar/types": ["@scalar/types@0.0.30", "", { "dependencies": { "@scalar/openapi-types": "0.1.7", "@unhead/schema": "^1.11.11" } }, "sha512-rhgwovQb5f7PXuUB5bLUElpo90fdsiwcOgBXVWZ6n6dnFSKovNJ7GPXQimsZioMzTF6TdwfP94UpZVdZAK4aTw=="],
+
+    "@typescript-eslint/typescript-estree/minimatch": ["minimatch@9.0.5", "", { "dependencies": { "brace-expansion": "^2.0.1" } }, "sha512-G6T0ZX48xgozx7587koeX9Ys2NYy6Gmv//P89sEte9V9whIapMNF4idKxnW2QtCcLiTWlb/wfCabAtAFWhhBow=="],
+
+    "color/color-convert": ["color-convert@1.9.3", "", { "dependencies": { "color-name": "1.1.3" } }, "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg=="],
+
+    "color-string/color-name": ["color-name@1.1.3", "", {}, "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw=="],
+
+    "fast-glob/glob-parent": ["glob-parent@5.1.2", "", { "dependencies": { "is-glob": "^4.0.1" } }, "sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow=="],
+
+    "@scalar/themes/@scalar/types/@scalar/openapi-types": ["@scalar/openapi-types@0.1.7", "", {}, "sha512-oOTG3JQifg55U3DhKB7WdNIxFnJzbPJe7rqdyWdio977l8IkxQTVmObftJhdNIMvhV2K+1f/bDoMQGu6yTaD0A=="],
+
+    "@typescript-eslint/typescript-estree/minimatch/brace-expansion": ["brace-expansion@2.0.1", "", { "dependencies": { "balanced-match": "^1.0.0" } }, "sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA=="],
+
+    "color/color-convert/color-name": ["color-name@1.1.3", "", {}, "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw=="],
+  }
+}
--- a/bun.lockb
+++ b/bun.lockb
--- a/docker-build.sh
+++ b/docker-build.sh
@@ -0,0 +1,64 @@
+#!/bin/bash
+
+# Enable error handling
+set -euo pipefail
+
+# Function to clean up on script exit
+cleanup() {
+    echo "Cleaning up..."
+    docker builder prune -f --filter until=24h
+    docker image prune -f
+}
+trap cleanup EXIT
+
+# Clean up Docker system
+echo "Cleaning up Docker system..."
+docker system prune -f --volumes
+
+# Set build arguments for better performance
+export DOCKER_BUILDKIT=1
+export COMPOSE_DOCKER_CLI_BUILD=1
+export BUILDKIT_PROGRESS=plain
+
+# Calculate available memory and CPU
+TOTAL_MEM=$(free -m | awk '/^Mem:/{print $2}')
+BUILD_MEM=$(( TOTAL_MEM / 2 ))  # Use half of available memory
+CPU_COUNT=$(nproc)
+CPU_QUOTA=$(( CPU_COUNT * 50000 ))  # Allow 50% CPU usage per core
+
+echo "Building with ${BUILD_MEM}MB memory limit and CPU quota ${CPU_QUOTA}"
+
+# Remove any existing lockfile
+rm -f bun.lockb
+
+# Build with resource limits, optimizations, and timeout
+echo "Building Docker image..."
+DOCKER_BUILDKIT=1 docker build \
+    --memory="${BUILD_MEM}m" \
+    --memory-swap="${BUILD_MEM}m" \
+    --cpu-quota="${CPU_QUOTA}" \
+    --build-arg BUILDKIT_INLINE_CACHE=1 \
+    --build-arg DOCKER_BUILDKIT=1 \
+    --build-arg NODE_ENV=production \
+    --progress=plain \
+    --no-cache \
+    --compress \
+    -t homeassistant-mcp:latest \
+    -t homeassistant-mcp:$(date +%Y%m%d) \
+    .
+
+# Check if build was successful
+BUILD_EXIT_CODE=$?
+if [ $BUILD_EXIT_CODE -eq 124 ]; then
+    echo "Build timed out after 15 minutes!"
+    exit 1
+elif [ $BUILD_EXIT_CODE -ne 0 ]; then
+    echo "Build failed with exit code ${BUILD_EXIT_CODE}!"
+    exit 1
+else
+    echo "Build completed successfully!"
+    
+    # Show image size and layers
+    docker image ls homeassistant-mcp:latest --format "Image size: {{.Size}}"
+    echo "Layer count: $(docker history homeassistant-mcp:latest | wc -l)"
+fi 
--- a/docker/speech/Dockerfile
+++ b/docker/speech/Dockerfile
@@ -13,8 +13,10 @@ RUN python -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"

 # Install Python dependencies with specific versions and CPU-only variants
-RUN pip install --no-cache-dir torch==2.1.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cpu
-RUN pip install --no-cache-dir faster-whisper==0.10.0 openwakeword==0.4.0 pyaudio==0.2.14 sounddevice==0.4.6
+RUN pip install --no-cache-dir "numpy>=1.24.3,<2.0.0" && \
+    pip install --no-cache-dir torch==2.1.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cpu && \
+    pip install --no-cache-dir faster-whisper==0.10.0 openwakeword==0.4.0 pyaudio==0.2.14 sounddevice==0.4.6 requests==2.31.0 && \
+    pip freeze > /opt/venv/requirements.txt

 # Create final image
 FROM python:3.10-slim
@@ -23,10 +25,14 @@ FROM python:3.10-slim
 COPY --from=builder /opt/venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"

-# Install only runtime dependencies
+# Install audio dependencies
 RUN apt-get update && apt-get install -y \
    portaudio19-dev \
    python3-pyaudio \
+    alsa-utils \
+    libasound2 \
+    libasound2-plugins \
+    pulseaudio \
    && rm -rf /var/lib/apt/lists/*

 # Create necessary directories
@@ -54,5 +60,9 @@ ENV PYTHONMALLOC=malloc \
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD ps aux | grep '[p]ython' || exit 1

-# Run the wake word detection service with resource constraints
-CMD ["python", "-X", "faulthandler", "wake_word_detector.py"] 
+# Copy audio setup script
+COPY setup-audio.sh /setup-audio.sh
+RUN chmod +x /setup-audio.sh
+
+# Start command
+CMD ["/bin/bash", "-c", "/setup-audio.sh && python -u wake_word_detector.py"] 
--- a/docker/speech/setup-audio.sh
+++ b/docker/speech/setup-audio.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+# Wait for PulseAudio to be ready
+sleep 2
+
+# Mute the monitor to prevent feedback
+pactl set-source-mute alsa_output.pci-0000_00_1b.0.analog-stereo.monitor 1
+
+# Set microphone sensitivity to 65%
+pactl set-source-volume alsa_input.pci-0000_00_1b.0.analog-stereo 65%
+
+# Set speaker volume to 40%
+pactl set-sink-volume alsa_output.pci-0000_00_1b.0.analog-stereo 40%
+
+# Make the script executable
+chmod +x /setup-audio.sh 
--- a/docker/speech/wake_word_detector.py
+++ b/docker/speech/wake_word_detector.py
@@ -8,46 +8,274 @@ from openwakeword import Model
 from datetime import datetime
 import wave
 from faster_whisper import WhisperModel
+import requests
+import logging
+import time
+
+# Set up logging
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)

 # Configuration
 SAMPLE_RATE = 16000
 CHANNELS = 1
 CHUNK_SIZE = 1024
-BUFFER_DURATION = 30  # seconds to keep in buffer
+BUFFER_DURATION = 10  # seconds to keep in buffer
 DETECTION_THRESHOLD = 0.5
+CONTINUOUS_TRANSCRIPTION_INTERVAL = 3  # seconds between transcriptions
+MAX_MODEL_LOAD_RETRIES = 3
+MODEL_LOAD_RETRY_DELAY = 5  # seconds
+MODEL_DOWNLOAD_TIMEOUT = 600  # 10 minutes timeout for model download

-# Wake word models to use
-WAKE_WORDS = ["hey_jarvis", "ok_google", "alexa"]
+# Audio processing parameters
+NOISE_THRESHOLD = 0.08  # Increased threshold for better noise filtering
+MIN_SPEECH_DURATION = 2.0  # Longer minimum duration to avoid fragments
+SILENCE_DURATION = 1.0  # Longer silence duration
+MAX_REPETITIONS = 1  # More aggressive repetition filtering
+ECHO_THRESHOLD = 0.75  # More sensitive echo detection
+MIN_SEGMENT_DURATION = 1.0  # Longer minimum segment duration
+FEEDBACK_WINDOW = 5  # Window size for feedback detection in seconds

-# Initialize the ASR model
-asr_model = WhisperModel(
-    model_size_or_path=os.environ.get('ASR_MODEL', 'base.en'),
-    device="cpu",
-    compute_type="int8",
-    download_root=os.environ.get('ASR_MODEL_PATH', '/models')
-)
+# Feature flags from environment
+WAKE_WORD_ENABLED = os.environ.get('ENABLE_WAKE_WORD', 'false').lower() == 'true'
+SPEECH_ENABLED = os.environ.get('ENABLE_SPEECH_FEATURES', 'true').lower() == 'true'
+
+# Wake word models to use (only if wake word is enabled)
+WAKE_WORDS = ["alexa"]  # Using 'alexa' as temporary replacement for 'gaja'
+WAKE_WORD_ALIAS = "gaja"  # What we print when wake word is detected
+
+# Home Assistant Configuration
+HASS_HOST = os.environ.get('HASS_HOST', 'http://homeassistant.local:8123')
+HASS_TOKEN = os.environ.get('HASS_TOKEN')
+
+def initialize_asr_model():
+    """Initialize the ASR model with retries and timeout"""
+    model_path = os.environ.get('ASR_MODEL_PATH', '/models')
+    model_name = os.environ.get('ASR_MODEL', 'large-v3')
+    
+    start_time = time.time()
+    for attempt in range(MAX_MODEL_LOAD_RETRIES):
+        try:
+            if time.time() - start_time > MODEL_DOWNLOAD_TIMEOUT:
+                logger.error("Model download timeout exceeded")
+                raise TimeoutError("Model download took too long")
+                
+            logger.info(f"Loading ASR model (attempt {attempt + 1}/{MAX_MODEL_LOAD_RETRIES})")
+            model = WhisperModel(
+                model_size_or_path=model_name,
+                device="cpu",
+                compute_type="int8",
+                download_root=model_path,
+                num_workers=1  # Reduce concurrent downloads
+            )
+            logger.info("ASR model loaded successfully")
+            return model
+        except Exception as e:
+            logger.error(f"Failed to load ASR model (attempt {attempt + 1}): {e}")
+            if attempt < MAX_MODEL_LOAD_RETRIES - 1:
+                logger.info(f"Retrying in {MODEL_LOAD_RETRY_DELAY} seconds...")
+                time.sleep(MODEL_LOAD_RETRY_DELAY)
+            else:
+                logger.error("Failed to load ASR model after all retries")
+                raise
+
+# Initialize the ASR model with retries
+try:
+    asr_model = initialize_asr_model()
+except Exception as e:
+    logger.error(f"Critical error initializing ASR model: {e}")
+    raise
+
+def send_command_to_hass(domain, service, entity_id):
+    """Send command to Home Assistant"""
+    if not HASS_TOKEN:
+        logger.error("Error: HASS_TOKEN not set")
+        return False
+
+    headers = {
+        "Authorization": f"Bearer {HASS_TOKEN}",
+        "Content-Type": "application/json",
+    }
+
+    url = f"{HASS_HOST}/api/services/{domain}/{service}"
+    data = {"entity_id": entity_id}
+
+    try:
+        response = requests.post(url, headers=headers, json=data)
+        response.raise_for_status()
+        logger.info(f"Command sent: {domain}.{service} for {entity_id}")
+        return True
+    except Exception as e:
+        logger.error(f"Error sending command to Home Assistant: {e}")
+        return False
+
+def is_speech(audio_data, threshold=NOISE_THRESHOLD):
+    """Detect if audio segment contains speech based on amplitude and frequency content"""
+    # Calculate RMS amplitude
+    rms = np.sqrt(np.mean(np.square(audio_data)))
+    
+    # Calculate signal energy in speech frequency range (100-4000 Hz)
+    fft = np.fft.fft(audio_data)
+    freqs = np.fft.fftfreq(len(audio_data), 1/SAMPLE_RATE)
+    speech_mask = (np.abs(freqs) >= 100) & (np.abs(freqs) <= 4000)
+    speech_energy = np.sum(np.abs(fft[speech_mask])) / len(audio_data)
+    
+    # Enhanced echo detection
+    # 1. Check for periodic patterns in the signal
+    autocorr = np.correlate(audio_data, audio_data, mode='full')
+    autocorr = autocorr[len(autocorr)//2:]  # Use only positive lags
+    peaks = np.where(autocorr > ECHO_THRESHOLD * np.max(autocorr))[0]
+    peak_spacing = np.diff(peaks)
+    has_periodic_echo = len(peak_spacing) > 2 and np.std(peak_spacing) < 0.1 * np.mean(peak_spacing)
+    
+    # 2. Check for sudden amplitude changes
+    amplitude_envelope = np.abs(audio_data)
+    amplitude_changes = np.diff(amplitude_envelope)
+    has_feedback_spikes = np.any(np.abs(amplitude_changes) > threshold * 2)
+    
+    # 3. Check frequency distribution
+    freq_magnitudes = np.abs(fft)[:len(fft)//2]
+    peak_freqs = freqs[:len(fft)//2][np.argsort(freq_magnitudes)[-3:]]
+    has_feedback_freqs = np.any((peak_freqs > 2000) & (peak_freqs < 4000))
+    
+    # Combine all criteria
+    is_valid_speech = (
+        rms > threshold and
+        speech_energy > threshold and
+        not has_periodic_echo and
+        not has_feedback_spikes and
+        not has_feedback_freqs
+    )
+    
+    return is_valid_speech
+
+def process_command(text):
+    """Process the transcribed command and execute appropriate action"""
+    text = text.lower().strip()
+    
+    # Skip if text is too short or contains numbers (likely noise)
+    if len(text) < 5 or any(char.isdigit() for char in text):
+        logger.debug("Text too short or contains numbers, skipping")
+        return
+    
+    # Enhanced noise pattern detection
+    noise_patterns = ["lei", "los", "und", "aber", "nicht mehr", "das das", "und und"]
+    for pattern in noise_patterns:
+        if text.count(pattern) > 1:  # More aggressive pattern filtering
+            logger.debug(f"Detected noise pattern '{pattern}', skipping")
+            return
+    
+    # More aggressive repetition detection
+    words = text.split()
+    if len(words) >= 2:
+        # Check for immediate word repetitions
+        for i in range(len(words)-1):
+            if words[i] == words[i+1]:
+                logger.debug(f"Detected immediate word repetition: '{words[i]}', skipping")
+                return
+        
+        # Check for phrase repetitions
+        phrases = [' '.join(words[i:i+2]) for i in range(len(words)-1)]
+        phrase_counts = {}
+        for phrase in phrases:
+            phrase_counts[phrase] = phrase_counts.get(phrase, 0) + 1
+            if phrase_counts[phrase] > MAX_REPETITIONS:
+                logger.debug(f"Skipping due to excessive repetition: '{phrase}'")
+                return
+    
+    # German command mappings
+    commands = {
+        "ausschalten": "turn_off",
+        "einschalten": "turn_on",
+        "an": "turn_on",
+        "aus": "turn_off"
+    }
+    
+    rooms = {
+        "wohnzimmer": "living_room",
+        "küche": "kitchen",
+        "schlafzimmer": "bedroom",
+        "bad": "bathroom"
+    }
+    
+    # Detect room
+    detected_room = None
+    for german_room, english_room in rooms.items():
+        if german_room in text:
+            detected_room = english_room
+            break
+    
+    # Detect command
+    detected_command = None
+    for german_cmd, english_cmd in commands.items():
+        if german_cmd in text:
+            detected_command = english_cmd
+            break
+    
+    if detected_room and detected_command:
+        # Construct entity ID (assuming light)
+        entity_id = f"light.{detected_room}"
+        
+        # Send command to Home Assistant
+        if send_command_to_hass("light", detected_command, entity_id):
+            logger.info(f"Executed: {detected_command} for {entity_id}")
+        else:
+            logger.error("Failed to execute command")
+    else:
+        logger.debug(f"No command found in text: '{text}'")

 class AudioProcessor:
    def __init__(self):
-        # Initialize wake word detection model
-        self.wake_word_model = Model(
-            custom_model_paths=None,  # Use default models
-            inference_framework="onnx"  # Use ONNX for better performance
-        )
-
-        # Pre-load the wake word models
-        for wake_word in WAKE_WORDS:
-            self.wake_word_model.add_model(wake_word)
-
+        logger.info("Initializing AudioProcessor...")
        self.audio_buffer = queue.Queue()
        self.recording = False
        self.buffer = np.zeros(SAMPLE_RATE * BUFFER_DURATION)
        self.buffer_lock = threading.Lock()
+        self.last_transcription_time = 0
+        self.stream = None
+        self.speech_detected = False
+        self.silence_frames = 0
+        self.speech_frames = 0
+
+        # Initialize wake word detection only if enabled
+        if WAKE_WORD_ENABLED:
+            try:
+                logger.info("Initializing wake word model...")
+                self.wake_word_model = Model(vad_threshold=0.5)
+                self.last_prediction = None
+                logger.info("Wake word model initialized successfully")
+            except Exception as e:
+                logger.error(f"Failed to initialize wake word model: {e}")
+                raise
+        else:
+            self.wake_word_model = None
+            self.last_prediction = None
+            logger.info("Wake word detection disabled")
+
+    def should_transcribe(self):
+        """Determine if we should transcribe based on mode and timing"""
+        current_time = datetime.now().timestamp()
+        if not WAKE_WORD_ENABLED:
+            # Check if enough time has passed since last transcription
+            time_since_last = current_time - self.last_transcription_time
+            if time_since_last >= CONTINUOUS_TRANSCRIPTION_INTERVAL:
+                # Only transcribe if we detect speech
+                frames_per_chunk = CHUNK_SIZE
+                min_speech_frames = int(MIN_SPEECH_DURATION * SAMPLE_RATE / frames_per_chunk)
+                
+                if self.speech_frames >= min_speech_frames:
+                    self.last_transcription_time = current_time
+                    self.speech_frames = 0  # Reset counter
+                    return True
+        return False

    def audio_callback(self, indata, frames, time, status):
        """Callback for audio input"""
        if status:
-            print(f"Audio callback status: {status}")
+            logger.warning(f"Audio callback status: {status}")
        
        # Convert to mono if necessary
        if CHANNELS > 1:
@@ -55,25 +283,45 @@ class AudioProcessor:
        else:
            audio_data = indata.flatten()

+        # Check for speech
+        if is_speech(audio_data):
+            self.speech_frames += 1
+            self.silence_frames = 0
+        else:
+            self.silence_frames += 1
+            frames_per_chunk = CHUNK_SIZE
+            silence_frames_threshold = int(SILENCE_DURATION * SAMPLE_RATE / frames_per_chunk)
+            
+            if self.silence_frames >= silence_frames_threshold:
+                self.speech_frames = 0
+
        # Update circular buffer
        with self.buffer_lock:
            self.buffer = np.roll(self.buffer, -len(audio_data))
            self.buffer[-len(audio_data):] = audio_data

-        # Process for wake word detection
-        prediction = self.wake_word_model.predict(audio_data)
-        
-        # Check if wake word detected
-        for wake_word in WAKE_WORDS:
-            if prediction[wake_word] > DETECTION_THRESHOLD:
-                print(f"Wake word detected: {wake_word} (confidence: {prediction[wake_word]:.2f})")
-                self.save_audio_segment(wake_word)
-                break
+        if WAKE_WORD_ENABLED:
+            # Process for wake word detection
+            self.last_prediction = self.wake_word_model.predict(audio_data)
+            
+            # Check if wake word detected
+            for wake_word in WAKE_WORDS:
+                confidence = self.last_prediction[wake_word]
+                if confidence > DETECTION_THRESHOLD:
+                    logger.info(
+                        f"Wake word: {WAKE_WORD_ALIAS} (confidence: {confidence:.2f})"
+                    )
+                    self.process_audio()
+                    break
+        else:
+            # Continuous transcription mode
+            if self.should_transcribe():
+                self.process_audio()

-    def save_audio_segment(self, wake_word):
-        """Save the audio buffer when wake word is detected"""
+    def process_audio(self):
+        """Process the current audio buffer (save and transcribe)"""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"/audio/wake_word_{wake_word}_{timestamp}.wav"
+        filename = f"/audio/audio_segment_{timestamp}.wav"
        
        # Save the audio buffer to a WAV file
        with wave.open(filename, 'wb') as wf:
@@ -85,89 +333,83 @@ class AudioProcessor:
            audio_data = (self.buffer * 32767).astype(np.int16)
            wf.writeframes(audio_data.tobytes())
        
-        print(f"Saved audio segment to {filename}")
+        logger.info(f"Saved audio segment to {filename}")

-        # Transcribe the audio
+        # Transcribe the audio with German language preference
        try:
            segments, info = asr_model.transcribe(
                filename,
-                language="en",
+                language="de",  # Set German as preferred language
                beam_size=5,
                temperature=0
            )
            
-            # Format the transcription result
-            result = {
-                "text": " ".join(segment.text for segment in segments),
-                "segments": [
-                    {
-                        "text": segment.text,
-                        "start": segment.start,
-                        "end": segment.end,
-                        "confidence": segment.confidence
-                    }
-                    for segment in segments
-                ]
-            }
+            # Get the full transcribed text
+            transcribed_text = " ".join(segment.text for segment in segments)
+            logger.info(f"Transcribed text: {transcribed_text}")
            
-            # Save metadata and transcription
-            metadata = {
-                "timestamp": timestamp,
-                "wake_word": wake_word,
-                "wake_word_confidence": float(prediction[wake_word]),
-                "sample_rate": SAMPLE_RATE,
-                "channels": CHANNELS,
-                "duration": BUFFER_DURATION,
-                "transcription": result
-            }
-            
-            with open(f"{filename}.json", 'w') as f:
-                json.dump(metadata, f, indent=2)
-                
-            print("\nTranscription result:")
-            print(f"Text: {result['text']}")
-            print("\nSegments:")
-            for segment in result["segments"]:
-                print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] ({segment['confidence']:.2%})")
-                print(f'"{segment["text"]}"')
+            # Process the command
+            process_command(transcribed_text)
                
        except Exception as e:
-            print(f"Error during transcription: {e}")
-            metadata = {
-                "timestamp": timestamp,
-                "wake_word": wake_word,
-                "wake_word_confidence": float(prediction[wake_word]),
-                "sample_rate": SAMPLE_RATE,
-                "channels": CHANNELS,
-                "duration": BUFFER_DURATION,
-                "error": str(e)
-            }
-            with open(f"{filename}.json", 'w') as f:
-                json.dump(metadata, f, indent=2)
+            logger.error(f"Error during transcription or processing: {e}")

    def start(self):
        """Start audio processing"""
        try:
-            print("Initializing wake word detection...")
-            print(f"Loaded wake words: {', '.join(WAKE_WORDS)}")
+            logger.info("Starting audio processor...")
            
-            with sd.InputStream(
-                channels=CHANNELS,
-                samplerate=SAMPLE_RATE,
-                blocksize=CHUNK_SIZE,
-                callback=self.audio_callback
-            ):
-                print("\nWake word detection started. Listening...")
-                print("Press Ctrl+C to stop")
-                
-                while True:
-                    sd.sleep(1000)  # Sleep for 1 second
+            # Log configuration
+            logger.debug(f"Sample Rate: {SAMPLE_RATE}")
+            logger.debug(f"Channels: {CHANNELS}")
+            logger.debug(f"Chunk Size: {CHUNK_SIZE}")
+            logger.debug(f"Buffer Duration: {BUFFER_DURATION}")
+            logger.debug(f"Wake Word Enabled: {WAKE_WORD_ENABLED}")
+            logger.debug(f"Speech Enabled: {SPEECH_ENABLED}")
+            logger.debug(f"ASR Model: {os.environ.get('ASR_MODEL')}")
+            
+            if WAKE_WORD_ENABLED:
+                logger.info("Initializing wake word detection...")
+                logger.info(f"Loaded wake words: {', '.join(WAKE_WORDS)}")
+            else:
+                logger.info("Starting continuous transcription mode...")
+                interval = CONTINUOUS_TRANSCRIPTION_INTERVAL
+                logger.info(f"Will transcribe every {interval} seconds")
+            
+            try:
+                logger.debug("Setting up audio input stream...")
+                with sd.InputStream(
+                    channels=CHANNELS,
+                    samplerate=SAMPLE_RATE,
+                    blocksize=CHUNK_SIZE,
+                    callback=self.audio_callback
+                ):
+                    logger.info("Audio input stream started successfully")
+                    logger.info("Listening for audio input...")
+                    logger.info("Press Ctrl+C to stop")
+                    
+                    while True:
+                        sd.sleep(1000)  # Sleep for 1 second
+                        
+            except sd.PortAudioError as e:
+                logger.error(f"Error setting up audio stream: {e}")
+                logger.error("Check if microphone is connected and accessible")
+                raise
+            except Exception as e:
+                logger.error(f"Unexpected error in audio stream: {e}")
+                raise
                    
        except KeyboardInterrupt:
-            print("\nStopping wake word detection...")
+            logger.info("\nStopping audio processing...")
        except Exception as e:
-            print(f"Error in audio processing: {e}")
+            logger.error("Critical error in audio processing", exc_info=True)
+            raise

 if __name__ == "__main__":
-    processor = AudioProcessor()
-    processor.start() 
+    try:
+        logger.info("Initializing AudioProcessor...")
+        processor = AudioProcessor()
+        processor.start()
+    except Exception as e:
+        logger.error("Failed to start AudioProcessor", exc_info=True)
+        raise 
--- a/docs/claude_desktop_config.json
+++ b/docs/claude_desktop_config.json
@@ -0,0 +1,16 @@
+{
+    "mcpServers": {
+        "homeassistant-mcp": {
+            "command": "bun",
+            "args": [
+                "run",
+                "start",
+                "--port",
+                "8080"
+            ],
+            "env": {
+                "NODE_ENV": "production"
+            }
+        }
+    }
+}
--- a/docs/cline_config.json
+++ b/docs/cline_config.json
@@ -0,0 +1,18 @@
+{
+    "mcpServers": {
+        "homeassistant-mcp": {
+            "command": "bun",
+            "args": [
+                "run",
+                "start",
+                "--enable-cline",
+                "--config",
+                "${configDir}/.env"
+            ],
+            "env": {
+                "NODE_ENV": "production",
+                "CLINE_MODE": "true"
+            }
+        }
+    }
+}
--- a/extra/claude-desktop-macos-setup.sh
+++ b/extra/claude-desktop-macos-setup.sh
@@ -326,4 +326,8 @@ if [[ $REPLY =~ ^[Yy]$ ]]; then
    else
        echo -e "${GREEN}Home Assistant MCP test successful!${NC}"
    fi
-fi 
+fi
+
+# macOS environment configuration
+HASS_SOCKET_URL="${HASS_HOST/http/ws}/api/websocket" # WebSocket URL conversion
+chmod 600 "$CLAUDE_CONFIG_DIR/claude_desktop_config.json" # Security hardening 
--- a/package.json
+++ b/package.json
@@ -21,7 +21,6 @@
    "profile": "bun --inspect src/index.ts",
    "clean": "rm -rf dist .bun coverage",
    "typecheck": "bun x tsc --noEmit",
-    "preinstall": "bun install --frozen-lockfile",
    "example:speech": "bun run examples/speech-to-text-example.ts"
  },
  "dependencies": {
@@ -38,6 +37,8 @@
    "node-fetch": "^3.3.2",
    "sanitize-html": "^2.11.0",
    "typescript": "^5.3.3",
+    "winston": "^3.11.0",
+    "winston-daily-rotate-file": "^5.0.0",
    "ws": "^8.16.0",
    "zod": "^3.22.4"
  },
--- a/scripts/start_mcp.cmd
+++ b/scripts/start_mcp.cmd
@@ -0,0 +1,21 @@
+@echo off
+setlocal
+
+:: Set environment variables
+set NODE_ENV=production
+
+:: Change to the script's directory
+cd /d "%~dp0"
+cd ..
+
+:: Start the MCP server
+echo Starting Home Assistant MCP Server...
+bun run start --port 8080
+
+if errorlevel 1 (
+    echo Error starting MCP server
+    pause
+    exit /b 1
+)
+
+pause 
--- a/smithery.yaml
+++ b/smithery.yaml
@@ -0,0 +1,28 @@
+# Smithery configuration file: https://smithery.ai/docs/config#smitheryyaml
+
+startCommand:
+  type: stdio
+  configSchema:
+    # JSON Schema defining the configuration options for the MCP.
+    type: object
+    required:
+      - hassToken
+    properties:
+      hassToken:
+        type: string
+        description: The token for connecting to Home Assistant API.
+      port:
+        type: number
+        default: 4000
+        description: The port on which the MCP server will run.
+  commandFunction:
+    # A function that produces the CLI command to start the MCP on stdio.
+    |-
+    config => ({
+      command: 'bun', 
+      args: ['--smol', 'run', 'start'], 
+      env: { 
+        HASS_TOKEN: config.hassToken,
+        PORT: config.port.toString()
+      }
+    })
--- a/src/config/app.config.ts
+++ b/src/config/app.config.ts
@@ -24,7 +24,7 @@ config({ path: resolve(process.cwd(), envFile) });
 */
 export const AppConfigSchema = z.object({
  /** Server Configuration */
-  PORT: z.number().default(4000),
+  PORT: z.coerce.number().default(4000),
  NODE_ENV: z
    .enum(["development", "production", "test"])
    .default("development"),
Author	SHA1	Message	Date
jango-blockchained	3f8d67b145	chore: refine configuration and setup scripts for improved usability - Update README with minor text formatting - Improve Smithery configuration command formatting - Enhance macOS setup script with WebSocket URL conversion and security hardening	2025-02-05 02:20:08 +01:00
jango-blockchained	ab8b597843	docs: add MCP client integration documentation and scripts - Update README with integration instructions for Cursor, Claude Desktop, and Cline - Add configuration examples for different MCP client integrations - Create Windows CMD script for starting MCP server - Include configuration files for Claude Desktop and Cline clients	2025-02-05 00:48:45 +01:00
jango-blockchained	ddf9070a64	Merge commit 'f5c01ad83a43dd6495b7906bee63a0652c9d1100'	2025-02-04 22:51:11 +01:00
jango-blockchained	b9727981cc	feat(speech): enhance speech processing with advanced audio setup and detection - Add audio setup script for PulseAudio configuration - Improve wake word detection with advanced noise filtering - Implement continuous transcription and command processing - Update speech Dockerfile with additional audio dependencies - Enhance logging and error handling in wake word detector	2025-02-04 22:51:06 +01:00
jango-blockchained	e1db799b1d	chore(dependencies): update Bun lockfile and package configuration - Update bun.lock with latest package versions - Modify Dockerfile to improve dependency installation - Remove preinstall script from package.json - Add winston logging dependencies - Adjust Docker build process for cleaner dependency management	2025-02-04 21:42:50 +01:00
smithery-ai[bot]	f5c01ad83a	Update README	2025-02-04 20:29:52 +00:00
smithery-ai[bot]	190915214d	Add Smithery configuration	2025-02-04 20:29:51 +00:00
jango-blockchained	905339fb67	refactor(docker): switch to Node.js base image and optimize Bun installation - Replace Bun base image with Node.js slim image - Install Bun globally using npm in both builder and runner stages - Simplify Docker build process and dependency management - Remove unnecessary environment variables and build flags - Update docker-build.sh to use BuildKit and remove lockfile before build	2025-02-04 20:18:46 +01:00
jango-blockchained	849b080aba	chore: update project dependencies and build configuration - Remove bun.lockb from version control - Add comprehensive docker-build.sh script for optimized Docker builds - Update Dockerfile with multi-stage build and improved resource management - Add winston logging dependencies to package.json - Enhance Docker image build process with resource constraints and caching	2025-02-04 20:14:13 +01:00