CryptoIntel MCP

Market Intelligence MCP Server

> A production-ready Model Context Protocol server for cryptocurrency market intelligence, powered by real-time exchange data, machine learning, and multi-agent orchestration.

[](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://modelcontextprotocol.io/)

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🚀 Features

30+ MCP Tools Across 11 Categories

• 📊 Real-Time Exchange Data - Binance, Kraken, Coinbase orderbooks & tickers
• 🔬 Market Microstructure - OFI, OBI, Microprice, VPIN analytics
• 🤖 ML Price Prediction - DeepLOB-Lite model for buy/sell signals
• 🎯 Trading Strategies - Multi-signal aggregation engine
• 👥 Multi-Agent System - Research, Risk, Execution agents with voting
• 📡 WebSocket Streaming - Real-time orderbook/ticker updates
• 🔔 Smart Alerts - Price-based notifications with background monitoring
• 💼 Portfolio Management - Risk analysis, P&L tracking, paper trading
• 🕵️ Anomaly Detection - Spoofing, layering, market regime classification
• 📈 Interactive Dashboard - Streamlit UI for live market visualization
• 🌐 Sentiment Analysis - Fear & Greed Index integration

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📋 Quick Start

Installation

# Clone repository
git clone https://github.com/Arshad-13/CryptoIntel-MCP.git
cd CryptoIntel-MCP

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Claude Desktop Integration

1. Copy configuration to Claude Desktop:

``bash cp docs/claude_desktop_config.json %APPDATA%\Claude\claude_desktop_config.json ``

1. Update paths in the config file to match your installation

1. Restart Claude Desktop

1. Test:

`` "Fetch orderbook for BTC/USDT" "Run analysis pipeline for ETH/USDT with sentiment 0.7" ``

Launch Dashboard

streamlit run dashboard.py

Open http://localhost:8501

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🎯 Use Cases

1. Market Analysis with Claude

You: "What's the current liquidity situation for BTC/USDT?"

Claude: [Fetches orderbook, calculates depth, analyzes spread]
"The BTC/USDT orderbook shows strong liquidity with 
$2.3M in bids within 0.5% of mid-price..."

2. ML-Driven Trading Signals

from tools.strategy_tools import get_trading_signal

signal = await get_trading_signal('ETH/USDT', sentiment_score=0.6)
# Returns: {'signal': 'BUY', 'confidence': 0.82, ...}

3. Multi-Agent Pipeline

You: "Run full analysis on SOL/USDT"

Claude: [Orchestrates Research → Risk → Execution agents]
"Research Agent: ML prediction BUY (78% confidence)
Risk Agent: Position size approved (2x BTC)
Execution Agent: Recommended entry: $142.35"

4. Real-Time Monitoring

• Dashboard: Live orderbook depth charts, ML predictions, portfolio P&L
• WebSocket Streams: Subscribe to orderbook/ticker updates
• Alerts: Get notified when BTC crosses $90,000

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🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                   Claude Desktop                         │
└────────────────────┬────────────────────────────────────┘
                     │ JSON-RPC / STDIO
┌────────────────────▼────────────────────────────────────┐
│          Market Intelligence MCP Server                 │
│                                                          │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐ │
│  │  Exchange    │  │  Analytics   │  │   Strategy   │ │
│  │   Tools      │  │    Engine    │  │    Engine    │ │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘ │
│         │  Direct HTTP     │  ML Models       │  Agents│ │
│  ┌──────▼──────────────────▼──────────────────▼───────┐ │
│  │   Binance  │  Kraken  │  Coinbase  │  WebSockets  │ │
│  └──────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘

Key Technologies:

• 🐍 Python 3.13 - Async I/O, type hints
• 🔌 httpx - Direct REST API calls (no CCXT overhead)
• ⚡ websockets - Real-time streaming
• 🧠 ONNX Runtime - ML model inference
• 📊 Streamlit - Interactive dashboard
• 💾 SQLite - Local persistence

See [Architecture Documentation](docs/ARCHITECTURE.md) for details

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📚 Documentation

| Document | Description | |----------|-------------| | [Installation Guide](docs/INSTALLATION.md) | Setup, configuration, troubleshooting | | [API Reference](docs/API.md) | Complete tool documentation with examples | | [Architecture](docs/ARCHITECTURE.md) | System design, data flows, scalability | | [Dashboard Guide](docs/DASHBOARD.md) | Dashboard features and customization | | [Changelog](docs/CHANGELOG.md) | Version history and feature timeline |

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🛠️ Development

Project Structure

CryptoIntel-MCP/
├── core/           # Business logic (analytics, ML, risk)
├── tools/          # MCP tool implementations
├── agents/         # Multi-agent system
├── tests/          # Test suite (pytest)
├── docs/           # Documentation
├── dashboard.py    # Streamlit UI
└── market_server.py # MCP server entry point

Running Tests

pytest tests/ -v

Coverage: 13 test files, 100+ test cases

Adding New Tools

1. Create function in tools/your_tool.py
2. Register in market_server.py:

``python @mcp.tool() def your_tool(param: str) -> str: return your_function(param) ``

1. Add tests in tests/test_your_tool.py

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🔧 Configuration

Environment Variables (.env)

# Optional: For premium APIs
CRYPTO_API_KEY=your_coingecko_api_key

Exchange Fallback

Automatic failover: Binance → Kraken → Coinbase

Configure in tools/exchange_tools.py:

EXCHANGE_FALLBACK_ORDER = ["binance", "kraken", "coinbase"]

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📊 Example Outputs

Orderbook Data

{
  "symbol": "BTC/USDT",
  "exchange": "binance",
  "bids": [[88360.79, 0.5], [88360.0, 1.2]],
  "asks": [[88361.0, 0.3], [88361.5, 0.8]],
  "fallback_used": false
}

Trading Signal

{
  "signal": "BUY",
  "confidence": 0.85,
  "components": {
    "ml_prediction": "buy",
    "ml_confidence": 0.78,
    "sentiment_score": 0.7,
    "risk_reward_ratio": 3.2
  }
}

Multi-Agent Pipeline

{
  "final_decision": "BUY",
  "confidence": 0.82,
  "agents": {
    "research": {"recommendation": "buy", "confidence": 0.78},
    "risk": {"approved": true, "max_size": 0.05},
    "execution": {"entry_price": 88360.0, "slippage": 0.02}
  }
}

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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🙏 Acknowledgments

• Model Context Protocol (MCP) - Anthropic's extensible AI integration framework
• DeepLOB - Limit order book prediction research
• Alternative.me - Fear & Greed Index data

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: [docs/](docs/)

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Built with ❤️ for the crypto trading community

Disclaimer: This is an educational project. Not financial advice. Trade at your own risk.

Source & license

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

• Author: Arshad-13
• Source: Arshad-13/CryptoIntel-MCP
• License: MIT

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