AI Agents with Tool Calling and MCP Servers Tutorial

Welcome to this comprehensive tutorial on building AI agents with tool calling capabilities and integrating Model Context Protocol (MCP) servers! This tutorial is designed for students who want to learn how to create intelligent agents that can interact with external tools and services.

What You'll Learn

By the end of this tutorial, you will be able to:

1. Understand AI Agents: Learn the fundamentals of AI agents and their architecture
2. Implement Tool Calling: Build agents that can call external tools and APIs
3. Work with MCP Servers: Integrate Model Context Protocol servers for enhanced capabilities
4. Connect to Real APIs: Use Slack and Google APIs through MCP servers
5. Build a Complete Agent: Create a functional agent that can perform real-world tasks

Tutorial Structure

This tutorial is divided into several parts:

Part 1: Foundations

• Understanding AI Agents
• Tool Calling Basics
• MCP Server Introduction

Part 2: Building Your Agent

• Setting Up Your Development Environment
• Creating Your First Agent
• Adding Tool Calling Capabilities

Part 3: MCP Integration

• Installing and Configuring MCP Servers
• Slack MCP Server Integration
• Google APIs MCP Server Integration

Part 4: Advanced Features

• Error Handling and Validation
• Agent Memory and Context Management
• Deployment and Best Practices

Prerequisites

Before starting this tutorial, you should have:

• Basic knowledge of Python programming
• Familiarity with command line tools
• OpenAI API key (get one at platform.openai.com)

Note: This tutorial uses mock MCP server implementations for learning purposes. Official MCP servers are still in development. See MCP Server Status for more details.

Quick Start

If you want to jump right in:

# Clone this repository
git clone <your-repo-url>
cd mcp_tutorial

# Install dependencies
pip install -r requirements.txt

# Start with Part 1
open docs/part1-foundations.md

Project Structure

mcp_tutorial/
├── docs/                    # Tutorial documentation
├── examples/                # Code examples for each part
├── agents/                  # Complete agent implementations
├── configs/                 # Configuration files
├── requirements.txt         # Python dependencies
└── README.md               # This file

Contributing

This tutorial is designed to be educational and collaborative. Feel free to:

• Submit issues for unclear sections
• Propose improvements
• Share your implementations

License

This tutorial is licensed under the MIT License - see the LICENSE file for details.

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Ready to start? Begin with Part 1: Foundations to understand the basics of AI agents!