docs: Refactor documentation structure and enhance project overview

- Update MkDocs configuration with streamlined navigation and theme improvements
- Revise README with comprehensive project introduction and key features
- Add new documentation pages for NLP, custom prompts, and extras
- Enhance index page with system architecture diagram and getting started guide
- Improve overall documentation clarity and organization

docs/nlp.md

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+# Natural Language Processing Guide 🤖
+
+## Overview
+
+My MCP Server includes powerful Natural Language Processing (NLP) capabilities powered by various AI models. This enables intelligent automation analysis, natural language control, and context-aware interactions with your Home Assistant setup.
+
+## Available Models 🎯
+
+### OpenAI Models
+- **GPT-4**
+  - Best for complex automation analysis
+  - Natural language understanding
+  - Context window: 8k-32k tokens
+  - Recommended for: Automation analysis, complex queries
+
+- **GPT-3.5-Turbo**
+  - Faster response times
+  - More cost-effective
+  - Context window: 4k tokens
+  - Recommended for: Quick commands, basic analysis
+
+### Claude Models
+- **Claude 2**
+  - Excellent code analysis
+  - Large context window (100k tokens)
+  - Strong system understanding
+  - Recommended for: Deep automation analysis
+
+### DeepSeek Models
+- **DeepSeek-Coder**
+  - Specialized in code understanding
+  - Efficient for automation rules
+  - Context window: 8k tokens
+  - Recommended for: Code generation, rule analysis
+
+## Configuration ⚙️
+
+```bash
+# AI Model Configuration
+PROCESSOR_TYPE=openai          # openai, claude, or deepseek
+OPENAI_MODEL=gpt-3.5-turbo    # or gpt-4, gpt-4-32k
+OPENAI_API_KEY=your_key_here
+
+# Optional: DeepSeek Configuration
+DEEPSEEK_API_KEY=your_key_here
+DEEPSEEK_BASE_URL=https://api.deepseek.com/v1
+
+# Analysis Settings
+ANALYSIS_TIMEOUT=30000         # Timeout in milliseconds
+MAX_RETRIES=3                 # Number of retries on failure
+```
+
+## Usage Examples 💡
+
+### 1. Automation Analysis
+
+```bash
+# Analyze an automation rule
+bun run analyze-automation path/to/automation.yaml
+
+# Example output:
+# "This automation triggers on motion detection and turns on lights.
+#  Potential issues:
+#  - No timeout for light turn-off
+#  - Missing condition for ambient light level"
+```
+
+### 2. Natural Language Commands
+
+```typescript
+// Send a natural language command
+const response = await fetch('http://localhost:3000/api/nlp/command', {
+  method: 'POST',
+  headers: {
+    'Content-Type': 'application/json',
+    'Authorization': `Bearer ${token}`
+  },
+  body: JSON.stringify({
+    command: "Turn on the living room lights and set them to warm white"
+  })
+});
+```
+
+### 3. Context-Aware Queries
+
+```typescript
+// Query with context
+const response = await fetch('http://localhost:3000/api/nlp/query', {
+  method: 'POST',
+  headers: {
+    'Content-Type': 'application/json',
+    'Authorization': `Bearer ${token}`
+  },
+  body: JSON.stringify({
+    query: "What's the temperature trend in the bedroom?",
+    context: {
+      timeframe: "last_24h",
+      include_humidity: true
+    }
+  })
+});
+```
+
+## Custom Prompts 📝
+
+You can customize the AI's behavior by creating custom prompts. See [Custom Prompts Guide](prompts.md) for details.
+
+Example custom prompt:
+```yaml
+name: energy_analysis
+description: Analyze home energy usage patterns
+prompt: |
+  Analyze the following energy usage data and provide:
+  1. Peak usage patterns
+  2. Potential optimizations
+  3. Comparison with typical usage
+  4. Cost-saving recommendations
+  
+  Context: {context}
+  Data: {data}
+```
+
+## Best Practices 🎯
+
+1. **Model Selection**
+   - Use GPT-3.5-Turbo for quick queries
+   - Use GPT-4 for complex analysis
+   - Use Claude for large context analysis
+   - Use DeepSeek for code-heavy tasks
+
+2. **Performance Optimization**
+   - Cache frequent queries
+   - Use streaming for long responses
+   - Implement retry logic for API calls
+
+3. **Cost Management**
+   - Monitor API usage
+   - Implement rate limiting
+   - Cache responses where appropriate
+
+4. **Error Handling**
+   - Implement fallback models
+   - Handle API timeouts gracefully
+   - Log failed queries for analysis
+
+## Advanced Features 🚀
+
+### 1. Chain of Thought Analysis
+```typescript
+const result = await analyzeWithCoT({
+  query: "Optimize my morning routine automation",
+  steps: ["Parse current automation", "Analyze patterns", "Suggest improvements"]
+});
+```
+
+### 2. Multi-Model Analysis
+```typescript
+const results = await analyzeWithMultiModel({
+  query: "Security system optimization",
+  models: ["gpt-4", "claude-2"],
+  compareResults: true
+});
+```
+
+### 3. Contextual Memory
+```typescript
+const memory = new ContextualMemory({
+  timeframe: "24h",
+  maxItems: 100
+});
+
+await memory.add("User typically arrives home at 17:30");
+```
+
+## Troubleshooting 🔧
+
+### Common Issues
+
+1. **Slow Response Times**
+   - Check model selection
+   - Verify API rate limits
+   - Consider caching
+
+2. **Poor Analysis Quality**
+   - Review prompt design
+   - Check context window limits
+   - Consider using a more capable model
+
+3. **API Errors**
+   - Verify API keys
+   - Check network connectivity
+   - Review rate limits
+
+## API Reference 📚
+
+See [API Documentation](api.md) for detailed endpoint specifications.