🚀 RL-MCP: Ryan's Model Context Protocol Server

Python FastAPI PostgreSQL Docker

🎯 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

Related MCP server: MCP Server Demo

📈 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

⚡ High-Performance Architecture

🚀 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:

📊 - Complete guide to stock market features
🔍 - Advanced semantic search capabilities
💡 - Real-world applications and code samples
🔗 - Complete endpoint 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:

make setup-environment

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:

🌐 API Server: http://localhost:8000
📚 API Docs: http://localhost:8000/docs
📈 Stock API: http://localhost:8000/v1/stock
🗄️ Database Admin: http://localhost:8080 (Adminer)

🐍 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:

📊 Swagger UI: http://localhost:8000/docs
📋 ReDoc: http://localhost:8000/redoc
🔍 OpenAPI Spec: http://localhost:8000/openapi.json

🔑 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? 🤔

📖 Check the API Documentation
📈 Explore Stock API Documentation
🐛 Open an Issue
💬 Start a Discussion

🚀 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 🤖💹

RL-MCP