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RL-MCP

by rlefko

πŸš€ 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 πŸŒ‰ that allows AI assistants to interact with your applications, databases, and APIs in a standardized, secure way.

πŸŽͺ Current Features

  • πŸ” Secure Authentication - Built-in auth system to protect your endpoints

  • πŸ“Š RESTful API - Clean, well-documented API endpoints with FastAPI

  • πŸ—„οΈ PostgreSQL Integration - Robust database layer with SQLModel/SQLAlchemy

  • 🐳 Docker Ready - Fully containerized development and deployment

  • πŸ”„ Database Migrations - Alembic-powered schema management

  • πŸ“ˆ Health Monitoring - Built-in health checks and connection monitoring

  • 🎨 Interactive Docs - Auto-generated API documentation

  • πŸ› οΈ Development Tools - Pre-commit hooks, linting, and formatting

πŸ“ˆ Stock Market Intelligence

πŸš€ 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

  • πŸ” Vector Search: Semantic search across news, analysis, and market data using advanced NLP

  • πŸ“Š Sentiment Analysis: Real-time sentiment scoring for news and market content

  • πŸ€– Smart Analysis: AI-driven stock analysis with confidence scoring and recommendations

  • 🎯 Relevance Scoring: Intelligent content ranking and filtering

πŸ’Ή Real-Time Market Data

  • πŸ“ˆ Live Pricing: Current stock prices with change indicators and market metrics

  • πŸ“° News Intelligence: Latest financial news with sentiment analysis from multiple sources

  • 🌍 Market Overview: Comprehensive market summaries with top movers and trends

  • πŸ”₯ Trending Analysis: Most active and discussed stocks based on data volume

⚑ High-Performance Architecture

  • πŸš€ Intelligent Caching: Multi-layer caching for lightning-fast responses

  • πŸ”„ Background Processing: Async data ingestion and processing

  • πŸ“Š Performance Monitoring: Built-in health checks and cache statistics

  • πŸ›‘οΈ Enterprise-Ready: Secure, scalable, and production-ready

🎯 Use Cases

  • πŸ€– AI Trading Assistants - Portfolio analysis and trading signals

  • πŸ“Š Financial Research - Market research and competitive intelligence

  • πŸ“± Investment Apps - Smart notifications and educational content

  • 🏒 Enterprise Systems - Risk management and client reporting

πŸ“š Comprehensive Documentation

Explore our detailed stock market API documentation:

  • πŸ“Š - Complete guide to stock market features

  • πŸ” - Advanced semantic search capabilities

  • πŸ’‘ - Real-world applications and code samples

  • πŸ”— - Complete endpoint documentation

πŸš€ Future Vision

This MCP server is designed to be the foundation for AI-powered applications that need:

  • πŸ€– AI Model Integration - Seamless connection between AI models and your data

  • πŸ”Œ Plugin Architecture - Extensible system for adding new capabilities

  • πŸ“‘ Real-time Communication - WebSocket support for live data streaming

  • 🌐 Multi-tenant Support - Serve multiple clients with isolated data

  • πŸ” Advanced Search - Vector search and semantic querying capabilities

  • πŸ“Š Analytics Dashboard - Monitor usage, performance, and insights

πŸ› οΈ Technology Stack

  • 🐍 Backend: Python 3.12 + FastAPI

  • πŸ—„οΈ Database: PostgreSQL with SQLModel

  • 🐳 Containerization: Docker + Docker Compose

  • πŸ”„ Migrations: Alembic

  • πŸ§ͺ Code Quality: Black, isort, pylint, pre-commit hooks

  • πŸ“š Documentation: Auto-generated OpenAPI/Swagger docs

  • 🧠 AI/ML: Sentence Transformers, Vector Search, Sentiment Analysis

πŸš€ 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:

  • πŸ”§ Install all required dependencies

  • 🐍 Create and configure a Python virtual environment

  • 🐳 Set up Docker containers

  • πŸ“¦ Install all Python packages

  • βœ… Verify everything is working

πŸƒβ€β™‚οΈ 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

πŸ“– API Documentation

Once running, explore the interactive API documentation:

πŸ”‘ Authentication

All API endpoints require authentication. Include your auth token in requests:

curl -H "Authorization: Bearer YOUR_TOKEN" http://localhost:8000/v1/item

πŸ“ˆ 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"

πŸ—„οΈ Database Management

πŸ”„ Creating Migrations

When you modify database models:

MSG="Add new awesome feature" make migration

πŸ—οΈ Database Commands

# Start the api and db at port 8000 make up # Check database health curl http://localhost:8000/health

πŸ› οΈ Development Workflow

πŸ“¦ Managing Dependencies

# Regenerate requirements.txt with latest versions make regen-requirements

🧹 Cleanup

# Remove all containers and volumes make clean

πŸ” Code Quality

Pre-commit hooks automatically run:

  • 🎨 Black - Code formatting

  • πŸ“‹ isort - Import sorting

  • πŸ” Pylint - Code linting

πŸ—οΈ Project Structure

rl-mcp/ β”œβ”€β”€ πŸ“ app/ # Main application code β”‚ β”œβ”€β”€ πŸ“ api/ # API layer β”‚ β”‚ └── πŸ“ v1/ # API version 1 β”‚ β”‚ β”œβ”€β”€ πŸ“ base/ # Base models and tables β”‚ β”‚ β”œβ”€β”€ πŸ“ item/ # Item management endpoints β”‚ β”‚ └── πŸ“ stock/ # πŸ“ˆ Stock market intelligence β”‚ β”‚ β”œβ”€β”€ πŸ“ services/ # AI services (vector search, market data) β”‚ β”‚ β”œβ”€β”€ πŸ“„ routes_stock.py # Stock API endpoints β”‚ β”‚ β”œβ”€β”€ πŸ“„ models_stock.py # Data models β”‚ β”‚ └── πŸ“„ controllers_stock.py # Business logic β”‚ β”œβ”€β”€ πŸ“ databases/ # Database configuration β”‚ └── πŸ“„ main.py # Application entry point β”œβ”€β”€ πŸ“ docs/ # πŸ“š Comprehensive documentation β”‚ └── πŸ“ stock/ # Stock API documentation β”œβ”€β”€ πŸ“ docker/ # Docker configurations β”œβ”€β”€ πŸ“ migrations/ # Database migrations β”œβ”€β”€ πŸ“ scripts/ # Utility scripts β”œβ”€β”€ πŸ“ utilities/ # Helper utilities └── πŸ“„ Makefile # Development commands

🀝 Contributing

We welcome contributions! πŸŽ‰

  1. 🍴 Fork the repository

  2. 🌿 Create a feature branch

  3. ✨ Make your changes

  4. πŸ§ͺ Run tests and linting

  5. πŸ“ Submit a pull request

πŸ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

πŸ†˜ Support

Having issues? πŸ€”


πŸš€ 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! ✨

πŸ“ˆ Featuring comprehensive stock market intelligence with AI-powered semantic search, real-time data, and intelligent caching πŸ€–πŸ’Ή

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