Context X Mcp

![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

# Clone the repository
git clone https://github.com/rnd-pro/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:

{
  "mcpServers": {
    "context-x-mcp": {
      "command": "node",
      "args": ["/path/to/context-x-mcp/src/server/index.js"],
      "env": {
        "NODE_ENV": "production"
      }
    }
  }
}

Basic Usage

// 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

# 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

# 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

// 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

// 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

# 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

• GitHub: rnd-pro
• 💼 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)
• Integrates with [Browser[X]MCP](https://github.com/rnd-pro/browser-x-mcp)
• Inspired by multi-agent AI architectures and distributed systems
• Natural language processing powered by advanced NLP libraries

📞 Support

• 📧 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📖 Documentation: [Repository docs](docs/)

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Made with ❤️ by RND-PRO Team for the AI context enrichment community

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: rnd-pro
• Source: rnd-pro/context-x-mcp
• License: MIT
• Homepage: https://www.npmjs.com/package/context-x-mcp

Install and usage instructions live in the source repository linked above.