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docs: enhance documentation with comprehensive API, architecture, and installation guides

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- Add detailed API documentation for core functions, SSE, and WebSocket APIs
- Create comprehensive architecture overview with system design diagrams
- Develop in-depth installation and quick start guides
- Improve troubleshooting documentation with advanced debugging techniques
- Update site navigation and markdown configuration
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# Architecture Documentation for MCP Server
---
layout: default
title: Architecture
nav_order: 4
---
## Overview
# Architecture Overview 🏗️
The MCP Server is designed as a high-performance, secure, and scalable bridge between Home Assistant and Language Learning Models (LLMs). This document outlines the architectural design principles, core components, and deployment strategies that power the MCP Server.
This document describes the architecture of the MCP Server, explaining how different components work together to provide a bridge between Home Assistant and Language Learning Models.
## Key Architectural Components
## System Architecture
### High-Performance Runtime with Bun
```mermaid
graph TD
subgraph "Client Layer"
WC[Web Clients]
MC[Mobile Clients]
VC[Voice Assistants]
end
- **Fast Startup & Efficiency:** Powered by Bun, the MCP Server benefits from rapid startup times, efficient memory utilization, and native TypeScript support.
- **Optimized Build Process:** Bun's build tools allow for quick iteration and deployment, ensuring minimal downtime and swift performance enhancement.
subgraph "MCP Server"
API[API Gateway]
NLP[NLP Engine]
SSE[SSE Manager]
WS[WebSocket Server]
CM[Command Manager]
SC[Scene Controller]
Cache[Redis Cache]
end
### Real-time Communication using Server-Sent Events (SSE)
subgraph "Home Assistant"
HA[Home Assistant Core]
Dev[Devices & Services]
end
- **Continuous Updates:** The server leverages SSE to deliver real-time notifications and updates, ensuring that any changes in Home Assistant are immediately communicated to connected clients.
- **Scalable Connection Handling:** SSE provides an event-driven model that efficiently manages multiple simultaneous client connections.
subgraph "AI Layer"
LLM[Language Models]
IC[Intent Classifier]
NER[Named Entity Recognition]
end
### Modular & Extensible Design
WC --> |HTTP/WS| API
MC --> |HTTP/WS| API
VC --> |HTTP| API
API --> |Events| SSE
API --> |Real-time| WS
API --> |Process| NLP
NLP --> |Query| LLM
NLP --> |Extract| IC
NLP --> |Identify| NER
CM --> |Execute| HA
HA --> |Control| Dev
SSE --> |State Updates| WC
SSE --> |State Updates| MC
WS --> |Bi-directional| WC
Cache --> |Fast Access| API
HA --> |Events| Cache
```
- **Plugin Architecture:** Designed with modularity in mind, the MCP Server supports plugins, add-ons, and custom automation scripts, enabling seamless feature expansion without disrupting core functionality.
- **Separation of Concerns:** Different components, such as device management, automation control, and system monitoring, are clearly separated, allowing independent development, testing, and scaling.
## Component Details
### Secure API Integration
### 1. Client Layer
- **Token-Based Authentication:** Robust token-based authentication mechanisms restrict access to authorized users and systems.
- **Rate Limiting & Error Handling:** Integrated rate limiting combined with comprehensive error handling ensures system stability and prevents misuse.
- **Best Practices:** All API endpoints follow industry-standard security guidelines to protect data and maintain system integrity.
The client layer consists of various interfaces that interact with the MCP Server:
### Deployment & Scalability
- **Web Clients**: Browser-based dashboards and control panels
- **Mobile Clients**: Native mobile applications
- **Voice Assistants**: Voice-enabled devices and interfaces
- **Containerized Deployment with Docker:** The use of Docker Compose enables straightforward deployment, management, and scaling of the server and its dependencies.
- **Flexible Environment Configuration:** Environment variables and configuration files (.env) facilitate smooth transitions between development, testing, and production setups.
### 2. MCP Server Core
## Future Enhancements
#### API Gateway
- Handles all incoming HTTP requests
- Manages authentication and rate limiting
- Routes requests to appropriate handlers
- **Advanced Automation Logic:** Integration of more complex automation rules and conditional decision-making capabilities.
- **Enhanced Security Measures:** Additional layers of security, such as multi-factor authentication and improved encryption techniques, are on the roadmap.
- **Improved Monitoring & Analytics:** Future updates will introduce advanced performance metrics and real-time analytics to monitor system health and user interactions.
```typescript
interface APIGateway {
authenticate(): Promise<boolean>;
rateLimit(): Promise<boolean>;
route(request: Request): Promise<Response>;
}
```
## Conclusion
#### NLP Engine
- Processes natural language commands
- Integrates with Language Models
- Extracts intents and entities
The architecture of the MCP Server prioritizes performance, scalability, and security. By leveraging Bun's high-performance runtime, employing real-time communication through SSE, and maintaining a modular, secure design, the MCP Server provides a robust platform for integrating Home Assistant with modern LLM functionalities.
```typescript
interface NLPEngine {
processCommand(text: string): Promise<CommandIntent>;
extractEntities(text: string): Promise<Entity[]>;
validateIntent(intent: CommandIntent): boolean;
}
```
*This document is a living document and will be updated as the system evolves.*
#### Event Management
- **SSE Manager**: Handles Server-Sent Events
- **WebSocket Server**: Manages bi-directional communication
- **Command Manager**: Processes and executes commands
## Key Components
### 3. Home Assistant Integration
- **API Module:** Handles RESTful endpoints, authentication, and error management.
- **SSE Module:** Provides real-time updates through Server-Sent Events.
- **Tools Module:** Offers various utilities for device control, automation, and data processing.
- **Security Module:** Implements token-based authentication and secure communications.
- **Integration Module:** Bridges data between Home Assistant and external systems.
The server maintains a robust connection to Home Assistant through:
- REST API calls
- WebSocket connections
- Event subscriptions
```typescript
interface HomeAssistantClient {
connect(): Promise<void>;
getState(entityId: string): Promise<EntityState>;
executeCommand(command: Command): Promise<CommandResult>;
subscribeToEvents(callback: EventCallback): Subscription;
}
```
### 4. AI Layer
#### Language Model Integration
- Processes natural language input
- Understands context and user intent
- Generates appropriate responses
#### Intent Classification
- Identifies command types
- Extracts parameters
- Validates requests
## Data Flow
1. Requests enter via the API endpoints.
2. Security middleware validates and processes requests.
3. Core modules process data and execute the necessary business logic.
4. Real-time notifications are managed by the SSE module.
### 1. Command Processing
## Future Enhancements
```mermaid
sequenceDiagram
participant Client
participant API
participant NLP
participant LLM
participant HA
Client->>API: Send command
API->>NLP: Process text
NLP->>LLM: Get intent
LLM-->>NLP: Return structured intent
NLP->>HA: Execute command
HA-->>API: Return result
API-->>Client: Send response
```
- Expand modularity with potential microservices.
- Enhance security with multi-factor authentication.
- Improve scalability through distributed architectures.
### 2. Real-time Updates
*Further diagrams and detailed breakdowns will be added in future updates.*
```mermaid
sequenceDiagram
participant HA
participant Cache
participant SSE
participant Client
HA->>Cache: State change
Cache->>SSE: Notify change
SSE->>Client: Send update
Note over Client: Update UI
```
### 3. [SSE API](api/sse.md)
- Event Subscriptions
- Real-time Updates
- Connection Management
## Security Architecture
### Authentication Flow
1. **JWT-based Authentication**
```typescript
interface AuthToken {
token: string;
expires: number;
scope: string[];
}
```
2. **Rate Limiting**
```typescript
interface RateLimit {
window: number;
max: number;
current: number;
}
```
### Security Measures
- TLS encryption for all communications
- Input sanitization
- Request validation
- Token-based authentication
- Rate limiting
- IP filtering
## Performance Optimizations
### Caching Strategy
```mermaid
graph LR
Request --> Cache{Cache?}
Cache -->|Hit| Response
Cache -->|Miss| HA[Home Assistant]
HA --> Cache
Cache --> Response
```
### Connection Management
- Connection pooling
- Automatic reconnection
- Load balancing
- Request queuing
## Configuration
The system is highly configurable through environment variables and configuration files:
```yaml
server:
port: 3000
host: '0.0.0.0'
homeAssistant:
url: 'http://homeassistant:8123'
token: 'YOUR_TOKEN'
security:
jwtSecret: 'your-secret'
rateLimit: 100
ai:
model: 'gpt-4'
temperature: 0.7
cache:
ttl: 300
maxSize: '100mb'
```
## Deployment Architecture
### Docker Deployment
```mermaid
graph TD
subgraph "Docker Compose"
MCP[MCP Server]
Redis[Redis Cache]
HA[Home Assistant]
end
MCP --> Redis
MCP --> HA
```
### Scaling Considerations
- Horizontal scaling capabilities
- Load balancing support
- Redis cluster support
- Multiple HA instance support
## Further Reading
- [API Documentation](api/index.md)
- [Installation Guide](getting-started/installation.md)
- [Contributing Guidelines](contributing.md)
- [Troubleshooting](troubleshooting.md)