Content
![Context[X]MCP Banner](assets/logo/context-x-mcp-banner.png)
**Multi-Agent Context Enrichment System with Auto-Detection and Tool Orchestration**
Context[X]MCP is a Model Context Provider (MCP) server that enables intelligent context enrichment through a multi-agent system with distributed specialized roles, auto-topic detection, and dynamic tool orchestration.
**Enrich your AI context automatically** - Works seamlessly with Cursor, Claude Desktop, VS Code, and other MCP-compatible applications while integrating Browser[X]MCP and other MCP tools.
## ✨ Features
### 🤖 **Multi-Agent Architecture**
- **Context Coordinator**: Intelligent topic detection and agent routing
- **Browser Research Agent**: Web research using Browser[X]MCP integration
- **Memory Agent**: Context history and pattern recognition
- **Tool Orchestrator**: Dynamic MCP tool discovery and management
- **Quality Assessment**: Context relevance scoring and verification
### 🧠 **Auto-Intelligence**
- **Topic Detection**: Automatic context classification and intent recognition
- **Tool Selection**: Dynamic selection of optimal MCP tools based on context
- **Context Enrichment**: Multi-source data gathering and synthesis
- **Pattern Learning**: Adaptive improvement based on usage patterns
### 🔄 **Agent Coordination**
- **Distributed Processing**: Specialized agents with narrow-focused roles
- **Task Distribution**: Intelligent workload balancing across agents
- **Result Aggregation**: Comprehensive context assembly from multiple sources
- **Conflict Resolution**: Smart handling of contradictory information
### 🌐 **Browser[X]MCP Integration**
- **Web Research**: Automated browser-based data collection
- **Real-time Extraction**: Dynamic content discovery and analysis
- **Form Interaction**: Advanced web form handling and data extraction
- **Link Analysis**: Intelligent navigation and content mapping
### 📊 **Context Management**
- **Vector Storage**: Efficient context history with similarity search
- **Relevance Scoring**: AI-powered context quality assessment
- **Memory Persistence**: Long-term context pattern storage
- **Performance Metrics**: Real-time agent coordination efficiency
### 💡 **Intelligent Orchestration**
- **Tool Discovery**: Automatic MCP tool capability mapping
- **Performance Optimization**: Response time and accuracy optimization
- **Resource Management**: Efficient agent resource allocation
- **Scalability**: Horizontal scaling for complex contexts
## 🚀 Quick Start
### Installation
```bash
# Clone the repository
git clone https://github.com/MakerDrive/context-x-mcp.git
cd context-x-mcp
# Install dependencies
npm install
# Copy environment configuration
cp .env.example .env
# Start the server
npm start
```
### MCP Client Configuration
Add to your MCP client configuration:
```json
{
"mcpServers": {
"context-x-mcp": {
"command": "node",
"args": ["/path/to/context-x-mcp/src/server/index.js"],
"env": {
"NODE_ENV": "production"
}
}
}
}
```
### Basic Usage
```javascript
// Example: Auto-enriched context request
await mcp.request("enrich_context", {
query: "Analyze current AI trends in browser automation",
depth: "comprehensive",
sources: ["web", "academic", "news"]
});
```
## 🏗️ Architecture
### Agent Communication Flow
```
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ MCP Client │───▶│ Context[X]MCP │───▶│ Browser[X]MCP │
│ (Cursor/CLI) │ │ Coordinator │ │ Agent │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
┌───────┴───────┐
▼ ▼
┌──────────────┐ ┌─────────────┐
│ Memory Agent │ │Tool Orch. │
│ History │ │ Agent │
└──────────────┘ └─────────────┘
│ │
▼ ▼
┌──────────────┐ ┌─────────────┐
│Quality Agent │ │Other MCP │
│ Assessment │ │ Tools │
└──────────────┘ └─────────────┘
```
### Multi-Agent Roles
1. **Context Coordinator Agent** - Main orchestration and routing
2. **Browser Research Agent** - Web-based data collection
3. **Context Memory Agent** - History and pattern management
4. **Tool Orchestrator Agent** - MCP tool coordination
5. **Quality Assessment Agent** - Result validation and scoring
## 🛠️ Available Tools
### Core Context Tools
- `enrich_context` - Comprehensive context enrichment
- `detect_topic` - Automatic topic classification
- `search_history` - Context history retrieval
- `assess_quality` - Context relevance scoring
### Agent Coordination Tools
- `route_request` - Intelligent agent routing
- `aggregate_results` - Multi-source result combination
- `optimize_performance` - System performance tuning
### Integration Tools
- `browser_research` - Browser[X]MCP integration
- `tool_discovery` - MCP tool capability mapping
- `pattern_analysis` - Usage pattern recognition
## ⚙️ Configuration
### Environment Variables
```bash
# MCP Server Configuration
MCP_PORT=3002
NODE_ENV=development
# Agent Configuration
AGENT_MAX_CONCURRENCY=5
AGENT_TIMEOUT=30000
# Context Settings
CONTEXT_HISTORY_SIZE=1000
CONTEXT_RELEVANCE_THRESHOLD=0.7
# Browser[X]MCP Integration
BROWSER_X_MCP_URL=http://localhost:3001
BROWSER_X_MCP_ENABLED=true
# Vector Storage
VECTOR_DB_PATH=./data/vectors
VECTOR_SIMILARITY_THRESHOLD=0.8
# Quality Assessment
QUALITY_MIN_SCORE=0.6
QUALITY_MAX_SOURCES=10
```
## 🧪 Testing
```bash
# Run all tests
npm test
# Test multi-agent coordination
npm run test:agents
# Test MCP integration
npm run test:integration
# Run mock tests (no external dependencies)
npm run test:mock
```
## 📁 Project Structure
```
context-x-mcp/
├── src/
│ ├── server/ # MCP server implementation
│ ├── agents/ # Multi-agent system
│ ├── core/ # Core functionality
│ └── utils/ # Utilities and helpers
├── test/ # Test suites
├── docs/ # Documentation
├── examples/ # Usage examples
└── assets/ # Assets and resources
```
## 🤝 Integration Examples
### With Browser[X]MCP
```javascript
// Automatic web research with form testing
const result = await contextXMCP.enrichContext({
query: "Research e-commerce checkout optimization",
enableBrowserResearch: true,
testForms: true,
maxSources: 5
});
```
### Multi-Tool Orchestration
```javascript
// Coordinate multiple MCP tools
const enrichedContext = await contextXMCP.orchestrateTools({
query: "Analyze competitor pricing strategies",
tools: ["browser-x-mcp", "data-analysis-mcp", "report-generator-mcp"],
coordination: "parallel"
});
```
## 🔮 Roadmap
### Phase 1: Foundation ✅
- [x] Project structure setup
- [x] Basic MCP server implementation
- [x] Agent framework foundation
### Phase 2: Core Agents (In Progress)
- [ ] Context Coordinator implementation
- [ ] Browser Research Agent integration
- [ ] Basic topic detection
### Phase 3: Advanced Features
- [ ] Vector-based context memory
- [ ] Quality assessment system
- [ ] Multi-tool orchestration
### Phase 4: Optimization
- [ ] Performance optimization
- [ ] Advanced pattern learning
- [ ] Production deployment
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](docs/CONTRIBUTING.md) for details.
### Development Setup
```bash
# Clone the repository
git clone https://github.com/rnd-pro/context-x-mcp.git
cd context-x-mcp
# Install dependencies
npm install
# Start development server
npm run dev
```
### Submitting Changes
1. Fork the repository
2. Create a feature branch: `git checkout -b feature/amazing-context-feature`
3. Commit changes: `git commit -m 'Add amazing context feature'`
4. Push to branch: `git push origin feature/amazing-context-feature`
5. Open a Pull Request
## 📄 License
MIT License - see [LICENSE](LICENSE) file for details.
## 👥 Development Team
**Developed by RND-PRO Team**
- 🌐 Website: [rnd-pro.com](https://rnd-pro.com)
- 💼 Professional development team specializing in innovative AI solutions
- 🤖 Experts in multi-agent systems and context enrichment technologies
- 🚀 Leaders in MCP protocol implementations and intelligent automation
## 🙏 Acknowledgments
- Built on [Model Context Protocol (MCP)](https://modelcontextprotocol.io/)
- Integrates with [Browser[X]MCP](https://github.com/MakerDrive/browser-x-mcp)
- Inspired by multi-agent AI architectures and distributed systems
- Natural language processing powered by advanced NLP libraries
## 📞 Support
- 📧 **Issues**: [GitHub Issues](https://github.com/rnd-pro/context-x-mcp/issues)
- 💬 **Discussions**: [GitHub Discussions](https://github.com/rnd-pro/context-x-mcp/discussions)
- 📖 **Documentation**: [Wiki](https://github.com/rnd-pro/context-x-mcp/wiki)
---
**Made with ❤️ by RND-PRO Team for the AI context enrichment community**
