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lets-learn-mcp-python

MCP Python Tutorial

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From the repo.

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.

Watch the video walkthrough on YouTube here: Let's Learn MCP Python Live

Code from the AI Engineer Paris Talk can be found here: Building MCP Servers for VS Code

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

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

Part 2: Using MCP Servers - Python Study Buddy

⏱️ 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 →

Part 3: Building Your Own MCP Server - AI Research Learning Hub

⏱️ 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. Visual Studio Code

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

2. Python 3.12+

  • 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

3. Python Extension for VS Code

  • Install the 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:

pip install uv

5. Create Virtual Environment

# 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

uv sync --active

6. Walk through the core concepts in the terminal

python part-1-concepts.py

7. Build your first MCP app

  1. Choose your path:

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

Happy learning! 🐍🧠

Collected info

  • ★ 1,068 stars
  • ⎇ 199 forks
  • Language: Python
  • Source updated: 9/24/2025

Config for your environment

Replace {MCP_ENDPOINT_URL} with this MCP’s endpoint URL (from its repo or docs above). No API key — you connect directly.

Tool

OS

Config file: ~/.cursor/mcp.json

{
  "mcpServers": {
    "mcp-server": {
      "url": "{MCP_ENDPOINT_URL}"
    }
  }
}

Paste into mcpServers in the config file. Restart Cursor after saving.

If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.