🚀 RL-MCP: Ryan's Model Context Protocol Server
🎯 A powerful, scalable Model Context Protocol (MCP) server built with modern Python technologies
🌟 What is RL-MCP?
RL-MCP is a robust Model Context Protocol server designed to provide AI models with structured access to external data and services. Think of it as a bridge 🌉 that allows AI assistants to interact with your applications, databases, and APIs in a standardized, secure way.
🎪 Current Features
- 🔐 Secure Authentication - Built-in auth system to protect your endpoints
- 📊 RESTful API - Clean, well-documented API endpoints with FastAPI
- 🗄️ PostgreSQL Integration - Robust database layer with SQLModel/SQLAlchemy
- 🐳 Docker Ready - Fully containerized development and deployment
- 🔄 Database Migrations - Alembic-powered schema management
- 📈 Health Monitoring - Built-in health checks and connection monitoring
- 🎨 Interactive Docs - Auto-generated API documentation
- 🛠️ Development Tools - Pre-commit hooks, linting, and formatting
📈 Stock Market Intelligence
🚀 Transform your applications with AI-powered financial intelligence
RL-MCP includes a comprehensive Stock Market Intelligence API that combines cutting-edge AI with real-time financial data:
🧠 AI-Powered Capabilities
- 🔍 Vector Search: Semantic search across news, analysis, and market data using advanced NLP
- 📊 Sentiment Analysis: Real-time sentiment scoring for news and market content
- 🤖 Smart Analysis: AI-driven stock analysis with confidence scoring and recommendations
- 🎯 Relevance Scoring: Intelligent content ranking and filtering
💹 Real-Time Market Data
- 📈 Live Pricing: Current stock prices with change indicators and market metrics
- 📰 News Intelligence: Latest financial news with sentiment analysis from multiple sources
- 🌍 Market Overview: Comprehensive market summaries with top movers and trends
- 🔥 Trending Analysis: Most active and discussed stocks based on data volume
- 🚀 Intelligent Caching: Multi-layer caching for lightning-fast responses
- 🔄 Background Processing: Async data ingestion and processing
- 📊 Performance Monitoring: Built-in health checks and cache statistics
- 🛡️ Enterprise-Ready: Secure, scalable, and production-ready
🎯 Use Cases
- 🤖 AI Trading Assistants - Portfolio analysis and trading signals
- 📊 Financial Research - Market research and competitive intelligence
- 📱 Investment Apps - Smart notifications and educational content
- 🏢 Enterprise Systems - Risk management and client reporting
📚 Comprehensive Documentation
Explore our detailed stock market API documentation:
🚀 Future Vision
This MCP server is designed to be the foundation for AI-powered applications that need:
- 🤖 AI Model Integration - Seamless connection between AI models and your data
- 🔌 Plugin Architecture - Extensible system for adding new capabilities
- 📡 Real-time Communication - WebSocket support for live data streaming
- 🌐 Multi-tenant Support - Serve multiple clients with isolated data
- 🔍 Advanced Search - Vector search and semantic querying capabilities
- 📊 Analytics Dashboard - Monitor usage, performance, and insights
🛠️ Technology Stack
- 🐍 Backend: Python 3.12 + FastAPI
- 🗄️ Database: PostgreSQL with SQLModel
- 🐳 Containerization: Docker + Docker Compose
- 🔄 Migrations: Alembic
- 🧪 Code Quality: Black, isort, pylint, pre-commit hooks
- 📚 Documentation: Auto-generated OpenAPI/Swagger docs
- 🧠 AI/ML: Sentence Transformers, Vector Search, Sentiment Analysis
🚀 Quick Start
Prerequisites
- 🐳 Docker and Docker Compose
- 🐍 Python 3.12+ (for local development)
- 🍺 Homebrew (macOS) or equivalent package manager
🎯 One-Command Setup
Get up and running in seconds! Our setup script handles everything:
This magical command will:
- 🔧 Install all required dependencies
- 🐍 Create and configure a Python virtual environment
- 🐳 Set up Docker containers
- 📦 Install all Python packages
- ✅ Verify everything is working
🏃♂️ Running the Application
🐳 Docker Development (Recommended)
# Build and start all services
make up
# Or run in background
docker compose up -d
Your services will be available at:
🐍 Local Development
# Activate virtual environment
source venv/bin/activate
# Start the api and db at port 8000
make up
📖 API Documentation
Once running, explore the interactive API documentation:
🔑 Authentication
All API endpoints require authentication. Include your auth token in requests:
curl -H "Authorization: Bearer YOUR_TOKEN" http://localhost:8000/v1/item
📈 Stock API Quick Example
# Search for Tesla battery technology insights
curl -X POST "http://localhost:8000/v1/stock/search" \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"query": "Tesla battery technology innovations",
"symbols": ["TSLA"],
"similarity_threshold": 0.8,
"limit": 10
}'
# Get current Apple stock price
curl -H "Authorization: Bearer YOUR_TOKEN" \
"http://localhost:8000/v1/stock/price/AAPL"
# Get market summary
curl -H "Authorization: Bearer YOUR_TOKEN" \
"http://localhost:8000/v1/stock/market/summary"
🗄️ Database Management
🔄 Creating Migrations
When you modify database models:
MSG="Add new awesome feature" make migration
🏗️ Database Commands
# Start the api and db at port 8000
make up
# Check database health
curl http://localhost:8000/health
🛠️ Development Workflow
📦 Managing Dependencies
# Regenerate requirements.txt with latest versions
make regen-requirements
🧹 Cleanup
# Remove all containers and volumes
make clean
🔍 Code Quality
Pre-commit hooks automatically run:
- 🎨 Black - Code formatting
- 📋 isort - Import sorting
- 🔍 Pylint - Code linting
🏗️ Project Structure
rl-mcp/
├── 📁 app/ # Main application code
│ ├── 📁 api/ # API layer
│ │ └── 📁 v1/ # API version 1
│ │ ├── 📁 base/ # Base models and tables
│ │ ├── 📁 item/ # Item management endpoints
│ │ └── 📁 stock/ # 📈 Stock market intelligence
│ │ ├── 📁 services/ # AI services (vector search, market data)
│ │ ├── 📄 routes_stock.py # Stock API endpoints
│ │ ├── 📄 models_stock.py # Data models
│ │ └── 📄 controllers_stock.py # Business logic
│ ├── 📁 databases/ # Database configuration
│ └── 📄 main.py # Application entry point
├── 📁 docs/ # 📚 Comprehensive documentation
│ └── 📁 stock/ # Stock API documentation
├── 📁 docker/ # Docker configurations
├── 📁 migrations/ # Database migrations
├── 📁 scripts/ # Utility scripts
├── 📁 utilities/ # Helper utilities
└── 📄 Makefile # Development commands
🤝 Contributing
We welcome contributions! 🎉
- 🍴 Fork the repository
- 🌿 Create a feature branch
- ✨ Make your changes
- 🧪 Run tests and linting
- 📝 Submit a pull request
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🆘 Support
Having issues? 🤔
🚀 Built with ❤️ for the future of AI-powered applications
Ready to revolutionize how AI models interact with your data? Let's build something amazing together! ✨
📈 Featuring comprehensive stock market intelligence with AI-powered semantic search, real-time data, and intelligent caching 🤖💹