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:

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

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

⏱️ 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 →

⏱️ 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 →

If you're ready to dive in immediately:

• Download and install VS Code
• Essential for MCP development and integration

• Install Python 3.12 or later from Python.org
• Verify installation: python --version or python3 --version
• Ensure pip is installed: pip --version or pip3 --version

• Install the Python extension
• Provides comprehensive Python development support
• Includes IntelliSense, debugging, and virtual environment management

To install UV, run the following command in the terminal:

pip install uv 

# Using venv (recommended)
python -m venv mcp-env
# Activate on macOS/Linux
source mcp-env/bin/activate
# Activate on Windows
mcp-env\Scripts\activate

uv sync --active

python part-1-concepts.py

2. Choose your path:
   • 🐍 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

• 📖 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

Happy learning! 🐍🧠