# Context X Mcp

> Multi-Agent Context Enrichment System with Auto-Detection and Tool Orchestration

- **Type:** MCP server
- **Install:** `agentstack add mcp-rnd-pro-context-x-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [rnd-pro](https://agentstack.voostack.com/s/rnd-pro)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [rnd-pro](https://github.com/rnd-pro)
- **Source:** https://github.com/rnd-pro/context-x-mcp
- **Website:** https://www.npmjs.com/package/context-x-mcp

## Install

```sh
agentstack add mcp-rnd-pro-context-x-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

![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/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:

```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](https://rnd-pro.com)**
- GitHub: [rnd-pro](https://github.com/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)](https://modelcontextprotocol.io/)
- 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](https://github.com/rnd-pro/context-x-mcp/issues)
- 💬 **Discussions**: [GitHub Discussions](https://github.com/rnd-pro/context-x-mcp/discussions)
- 📖 **Documentation**: [Repository docs](docs/)

---

**Made with ❤️ by [RND-PRO Team](https://rnd-pro.com) 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](https://github.com/rnd-pro)
- **Source:** [rnd-pro/context-x-mcp](https://github.com/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.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-rnd-pro-context-x-mcp
- Seller: https://agentstack.voostack.com/s/rnd-pro
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.