A comprehensive guide to understanding and building Model Context Protocol (MCP) Servers for Python developers through interactive learning experiences.
By the end of this tutorial series, you'll have:
- 🐍 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
- 🧠 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
⏱️ 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
- Verify your development environment
Start here: Part 1: Prerequisites and Setup →
- Note for a more in depth 'Getting Started' with MCP Demo check out mcp-python-demo
⏱️ Time: 20-35 minutes
Key Learning Objectives:
- Create a basic MCP server in Python
- Use prompts with MCP
- 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 →
⏱️ Time: 20-35 minutes
Key Learning Objectives:
- Find and use external MCP servers
- Add resources with MCP
- 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 topicget_trending_papers()
- Discover what's hot in AI researchcreate_study_plan()
- Generate personalized learning roadmapssummarize_paper()
- Create digestible summaries of complex researchtrack_learning_progress()
- Monitor study achievements and goalssend_research_learning()
- Send study daily study note to the user
Continue to: Part 3: AI Research Learning Hub →
If you're ready to dive in immediately:
- Prerequisites: Ensure you have VS Code, Python 3.12+, Docker and Python extension installed
- Choose your path:
- 🆕 Need help getting set up? Start with Part 1: Setup
- 🐍 Want to learn Python with MCP? Jump to Part 2: Python Study Buddy
- 🧠 Ready to build AI research tools? Go to Part 3: AI Research Hub
letslearnmcp-python/
├── README.md # This overview
├── part1-setup.md # Prerequisites and environment setup
├── part2-study-buddy.md # Building Python learning companion
└── part3-ai-researcher.md # Creating AI research discovery server
- 📖 MCP Official Documentation
- 🛠️ Python MCP SDK Repository
- 🐍 Python MCP Examples
- 🧠 Quick Start Python MCP Demo
- 📚 ArXiv API Documentation
- 🔬 Papers With Code API
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
Ready to get started? Begin with Part 1: Prerequisites and Setup →
Happy learning! 🐍🧠