context-x-mcp/docs/architecture.md

Multi-agent context enrichment system with auto-topic detection, auto-tool selection, and distributed specialized roles - A Model Context Provider (MCP) server for intelligent context management

# Context[X]MCP Architecture

## Overview

Context[X]MCP is a multi-agent system designed for intelligent context enrichment through distributed specialized roles, auto-topic detection, and dynamic tool orchestration.

## System Architecture

### High-Level Components

```
┌─────────────────────────────────────────────────────────┐
│                   MCP Client Layer                      │
│           (Cursor, Claude Desktop, VS Code)             │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│                Context[X]MCP Server                     │
│  ┌─────────────────────────────────────────────────┐    │
│  │            Context Coordinator Agent             │    │
│  │         (Main Orchestration & Routing)          │    │
│  └─────────────────────┬───────────────────────────┘    │
│                        │                                │
│  ┌─────────────────────▼───────────────────────────┐    │
│  │              Multi-Agent System                 │    │
│  │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │    │
│  │ │   Browser   │ │   Memory    │ │Tool Orch.   │ │    │
│  │ │ Research    │ │   Agent     │ │   Agent     │ │    │
│  │ │   Agent     │ │             │ │             │ │    │
│  │ └─────────────┘ └─────────────┘ └─────────────┘ │    │
│  │ ┌─────────────┐                                 │    │
│  │ │  Quality    │                                 │    │
│  │ │ Assessment  │                                 │    │
│  │ │   Agent     │                                 │    │
│  │ └─────────────┘                                 │    │
│  └─────────────────────────────────────────────────┘    │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│              External MCP Services                      │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
│ │Browser[X]   │ │  Vector     │ │    Other MCP        │ │
│ │   MCP       │ │  Storage    │ │     Tools           │ │
│ └─────────────┘ └─────────────┘ └─────────────────────┘ │
└─────────────────────────────────────────────────────────┘
```

## Agent Specifications

### 1. Context Coordinator Agent

**Role**: Main orchestration and intelligent routing

**Responsibilities**:
- Topic detection and classification
- Agent selection based on query analysis
- Task distribution and coordination
- Result aggregation and synthesis
- Quality control and validation

**Key Features**:
- NLP-based intent recognition
- Dynamic agent routing algorithms
- Performance optimization
- Error handling and recovery

### 2. Browser Research Agent

**Role**: Web-based data collection and analysis

**Responsibilities**:
- Integration with Browser[X]MCP
- Automated web research
- Content extraction and validation
- Link analysis and discovery
- Real-time data gathering

**Key Features**:
- Form interaction capabilities
- Dynamic content handling
- Screenshot analysis
- Navigation optimization

### 3. Context Memory Agent

**Role**: Historical context management and pattern recognition

**Responsibilities**:
- Context history storage
- Vector-based similarity search
- Pattern recognition and learning
- Long-term memory management
- Context retrieval optimization

**Key Features**:
- Vector embeddings
- Similarity scoring
- Memory cleanup and optimization
- Pattern analysis

### 4. Tool Orchestrator Agent

**Role**: MCP tool discovery and coordination

**Responsibilities**:
- MCP tool capability mapping
- Dynamic tool discovery
- Performance-based selection
- Resource management
- Tool integration

**Key Features**:
- Auto-discovery mechanisms
- Capability assessment
- Load balancing
- Error handling

### 5. Quality Assessment Agent

**Role**: Context validation and scoring

**Responsibilities**:
- Content relevance scoring
- Information verification
- Completeness evaluation
- Quality metrics tracking
- Result optimization

**Key Features**:
- Relevance algorithms
- Verification methods
- Quality metrics
- Performance tracking

## Data Flow

### Context Enrichment Flow

1. **Request Reception**
   - MCP client sends enrichment request
   - Context Coordinator receives and analyzes query

2. **Topic Detection**
   - NLP analysis of query content
   - Intent and category classification
   - Confidence scoring

3. **Agent Selection**
   - Based on topic analysis
   - Resource availability
   - Performance metrics

4. **Parallel Execution**
   - Selected agents execute in parallel
   - Real-time progress monitoring
   - Error handling and recovery

5. **Result Aggregation**
   - Results collected from all agents
   - Quality assessment and scoring
   - Conflict resolution

6. **Response Synthesis**
   - Final context compilation
   - Formatting and optimization
   - Response delivery

## Configuration Architecture

### Environment-Based Configuration

```javascript
// Core MCP Settings
MCP_PORT=3002
NODE_ENV=development

// Agent Configuration
AGENT_MAX_CONCURRENCY=5
AGENT_TIMEOUT=30000

// Context Management
CONTEXT_HISTORY_SIZE=1000
CONTEXT_RELEVANCE_THRESHOLD=0.7

// External Integrations
BROWSER_X_MCP_URL=http://localhost:3001
BROWSER_X_MCP_ENABLED=true

// Performance Optimization
VECTOR_SIMILARITY_THRESHOLD=0.8
QUALITY_MIN_SCORE=0.6
```

### Dynamic Configuration

- Runtime parameter adjustment
- Agent-specific configurations
- Performance-based optimizations
- Resource allocation tuning

## Integration Patterns

### Browser[X]MCP Integration

```javascript
// Browser Research Agent integration
const browserAgent = new BrowserResearchAgent({
  browserXMcpUrl: config.get('BROWSER_X_MCP_URL'),
  timeout: config.getNumber('BROWSER_X_MCP_TIMEOUT'),
  enableFormTesting: true,
  screenshotAnalysis: true
});
```

### Vector Storage Integration

```javascript
// Memory Agent vector operations
const memoryAgent = new ContextMemoryAgent({
  vectorDbPath: config.get('VECTOR_DB_PATH'),
  similarityThreshold: config.getFloat('VECTOR_SIMILARITY_THRESHOLD'),
  maxResults: config.getNumber('VECTOR_MAX_RESULTS')
});
```

## Scalability Considerations

### Horizontal Scaling

- Agent instance multiplication
- Load balancing across agents
- Resource pool management
- Dynamic scaling based on demand

### Performance Optimization

- Parallel processing maximization
- Cache utilization
- Memory management
- Network optimization

### Resource Management

- CPU and memory monitoring
- Agent resource allocation
- Garbage collection optimization
- Connection pooling

## Security Architecture

### Data Protection

- Local processing priority
- Encrypted inter-agent communication
- Secure configuration management
- Access control mechanisms

### Error Handling

- Graceful degradation
- Circuit breaker patterns
- Retry mechanisms
- Logging and monitoring

## Development Roadmap

### Phase 1: Foundation ✅
- Basic MCP server
- Agent framework
- Configuration system

### Phase 2: Core Agents (Current)
- Context Coordinator implementation
- Topic detection algorithms
- Basic agent communication

### Phase 3: Advanced Features
- Browser[X]MCP integration
- Vector-based memory
- Quality assessment system

### Phase 4: Optimization
- Performance tuning
- Advanced pattern learning
- Production deployment