MCP Tutorial

This repository contains the tutorial materials and example code for my YouTube course on MCP (Model Context Protocol). The course walks through the evolution of tool use in AI — from Toolformer to MCP — and then dives into practical examples of building MCP servers, clients, and agents.

📺 Course Overview

This repository follows along with the YouTube tutorial series: self deep learning - MCP Course

Chapter 1 — How AI Uses Tools: From Toolformer to MCP

• Who this course is for & prerequisites
• What a Tool is & why tools are needed
• Classical Tool Use – Toolformer (2023)
• Transition to MCP

Chapter 2 — MCP Fundamentals

• Core Components — Host, Client, Server
• tools/list request
• tools/call request
• Protocol Mechanics — JSON-RPC 2.0
• Relationship with Function Calling Features

Chapter 3 — MCP Basics in Python

• Setting up the MCP stdio server
• Defining MCP tools (e.g., Calculator)
• Setting up the MCP stdio client
• Calling MCP server tools from the client
• Setting up the MCP streamable HTTP server
• Setting up the MCP HTTP client

Chapter 4 — Connecting to Existing MCP Servers

• Connecting to built-in servers such as Filesystem, Git, and Tavily

Chapter 5 — MCP + LLM Integration

• How to connect MCP to an LLM (e.g., OpenAI API, LangChain/LangGraph)
• Security risks and safe design considerations

Chapter 6 — Future Outlook & Closing

• What we didn’t cover and why
• Future outlook for MCP and related approaches

📂 Directory Structure

.
├── servers/     # MCP servers (stdio, HTTP, etc.)
├── clients/     # MCP clients (stdio, HTTP, etc.)
├── agents/      # Chatbot agents using OpenAI API or LangChain
└── assets/      # Slides & YouTube scripts

🚀 Getting Started

1. Install uv

Follow the instructions here: uv installation guide.

For example, on macOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

On Windows (PowerShell):

irm https://astral.sh/uv/install.ps1 | iex

2. Create Virtual Environment

uv venv
source .venv/bin/activate   # On macOS/Linux
.venv\Scripts\activate      # On Windows

3. Install Dependencies

uv sync

4. Run an Example

uv run clients/stdio_client.py

5. Environment Variables

Some examples(tavily_client, agents) in this project require API keys for external services (e.g., LLMs, search tools).

1. Copy .env.example to .env:

cp .env.example .env

2. Edit .env and replace with your own keys:

TAVILY_API_KEY=tvly-xxxxxx
OPENAI_API_KEY=sk-xxxxxx

You can get your keys here:

👉 Tavily API Key → https://app.tavily.com/

👉 OpenAI API Key → https://platform.openai.com/

🎯 Learning Goals

By the end of this tutorial project, you will:

• Understand what MCP is and why it matters.
• Learn how to build your own MCP servers and clients.
• Connect existing servers (Filesystem, Git, Tavily).
• Integrate MCP with LLMs safely.

📌 Notes

• This repo is designed to be educational, not production-ready.
• The assets/ folder contains slides and scripts used in the videos.