🚀 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

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! 🎉

1. 🍴 Fork the repository

2. 🌿 Create a feature branch

3. ✨ Make your changes

4. 🧪 Run tests and linting

5. 📝 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 🤖💹