# lets-learn-mcp-python **Repository Path**: mirrors_lepy/lets-learn-mcp-python ## Basic Information - **Project Name**: lets-learn-mcp-python - **Description**: MCP Python Tutorial - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-08-22 - **Last Updated**: 2025-08-25 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Let's Learn MCP with Python - Tutorial Series A comprehensive guide to understanding and building Model Context Protocol (MCP) Servers for Python developers through interactive learning experiences. ## What You'll Build By the end of this tutorial series, you'll have: 1. **🐍 Python Study Buddy App** - An interactive console application that uses a custom MCP server to help developers learn Python concepts at beginner, intermediate, and expert levels 2. **🧠 AI Research Learning MCP Server** - Your own advanced MCP server that helps AI assistants find the latest AI/ML research papers, highlight top discoveries, and create personalized study plans ## Tutorial Structure ### [Part 1: Setup and Core Concepts](#quick-start) mcp-core-concepts **⏱️ Time: 15-20 minutes** Set up your development environment and understand MCP fundamentals: - Install VS Code, Python 3.12+, and Python extension - Learn what Model Context Protocol is and why it matters - Understand the client-server architecture * Note for a more in depth 'Getting Started' with MCP Demo check out [mcp-python-demo](https://github.com/pamelafox/mcp-python-demo) --- ### [Part 2: Using MCP Servers - Python Study Buddy](part2-study-buddy-python.md) **⏱️ Time: 20-35 minutes** study-buddy-app **Key Learning Objectives:** 1. Create a basic MCP server in Python 2. Use prompts with MCP 3. Use basic tools with MCP **Outcomes:** Building an interactive Python learning companion: - Configure a custom Python Learning MCP server - Create Python models for learning concepts using dataclasses - Build an interactive study session with progress tracking - Generate personalized coding challenges and explanations - Understand how AI assistants can enhance learning experiences **Example of what you'll create**: ``` 🐍 Python Study Buddy - Interactive Learning Session =================================================== Level: Intermediate Topic: List Comprehensions Progress: 3/10 concepts mastered Challenge: Create a list comprehension that filters even numbers... 💡 Hint: Use the modulo operator (%) to check for even numbers 🎯 Your mission: Write code that demonstrates understanding! ``` **Continue to**: [Part 2: Python Study Buddy →](part2-study-buddy.md) --- ### [Part 3: Building Your Own MCP Server - AI Research Learning Hub](part3-ai-researcher.md) **⏱️ Time: 20-35 minutes** **Key Learning Objectives:** 1. Find and use external MCP servers 2. Add resources with MCP 3. Automate Tasks with MCP **Outcomes:** Build an advanced MCP server that helps you keep up with the latest AI Research: - Create an AI/ML research paper discovery service - Implement tools for finding trending papers and breakthroughs - Build personalized study plan generation capabilities - Create intelligent content summarization and ranking - Store daily AI Research Learning Notes in a Github Repo **What you'll build**: - `search_research_papers()` - Find latest AI/ML research by topic - `get_trending_papers()` - Discover what's hot in AI research - `create_study_plan()` - Generate personalized learning roadmaps - `summarize_paper()` - Create digestible summaries of complex research - `track_learning_progress()` - Monitor study achievements and goals - `send_research_learning()` - Send study daily study note to the user **Continue to**: [Part 3: AI Research Learning Hub →](./part3-ai-researcher.md) --- ## Quick Start If you're ready to dive in immediately: ### 1. Visual Studio Code - Download and install [VS Code](https://code.visualstudio.com/) - Essential for MCP development and integration ### 2. Python 3.12+ - Install Python 3.12 or later from [Python.org](https://www.python.org/downloads/) - Verify installation: `python --version` or `python3 --version` - Ensure pip is installed: `pip --version` or `pip3 --version` ### 3. Python Extension for VS Code - Install the [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) extension - Provides comprehensive Python development support - Includes IntelliSense, debugging, and virtual environment management ### 4. Install UV To install UV, run the following command in the terminal: ```bash pip install uv ``` ### 5. Create Virtual Environment ```bash # Using venv (recommended) python -m venv mcp-env # Activate on macOS/Linux source mcp-env/bin/activate # Activate on Windows mcp-env\Scripts\activate ``` ### 6. Install packages ```bash uv sync --active ``` ### 6. Walk through the core concepts in the terminal ```bash python part-1-concepts.py ``` ### 7. Build your first MCP app 2. **Choose your path**: - 🐍 **Want to learn Python with MCP?** Jump to [Part 2: Python Study Buddy](part2-study-buddy.md) - 🧠 **Ready to build AI research tools?** Go to [Part 3: AI Research Hub](part3-ai-researcher.md) ## Additional Resources - 📖 [MCP Official Documentation](https://modelcontextprotocol.io/) - 🛠️ [Python MCP SDK Repository](https://github.com/modelcontextprotocol/python-sdk) - 🐍 [Python MCP Examples](https://github.com/modelcontextprotocol/servers) - 🧠 [Quick Start Python MCP Demo](https://github.com/pamelafox/mcp-python-demo) - 📚 [ArXiv API Documentation](https://arxiv.org/help/api/user-manual) - 🔬 [Papers With Code API](https://paperswithcode.com/api/v1/docs/) ## Contributing This tutorial is open source! Feel free to: - 🐛 Submit improvements and corrections - 💡 Add more examples and use cases - 🤝 Share your own MCP server implementations - 💬 Help others in the discussions ---- *Happy learning! 🐍🧠*