Skip to content

NickAzureDevops/learnmcp-python

Repository files navigation

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

⏱️ 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:

  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 →


Quick Start

If you're ready to dive in immediately:

  1. Prerequisites: Ensure you have VS Code, Python 3.12+, Docker and Python extension installed
  2. Choose your path:

Repository Structure

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

Additional Resources

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

Ready to get started? Begin with Part 1: Prerequisites and Setup →

Happy learning! 🐍🧠

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages