MCP & A2A – Model Context Protocol & Agent to Agent Protocol | Udemy

New – A2A or Agent2Agent Protocol components, lifecycle and code implementations added. Connect 3 Agents via A2A. Learn the A2A Python SDK

TLDR – In this course, you’ll learn about MCP & A2A – Model Context Protocol & Agent2Agent Protocol. We will build 5 MCP Clients and 3 MCP Servers from Scratch. You’ll build a UI for MCP client in Streamlit in Python. You’ll learn to deploy your MCP Server to Google Cloud using Server Sent Events. To top it all, we’ll use a free Gemini API Key from Google so you don’t need to pay for AI Models when learning! We then build A2A Agents, Clients and Servers and use Google’s ADK or Agent Development Kit

Key Objectives of Course

1. Build A2A Servers, Clients and Agents (using Google’s ADK or agent development kit) – in Beta
2. Connect 3 Agents via A2A with full code implementation
3. A2A Python SDK
4. Build 5 MCP Clients:
   1. #1. Basic Python + Gemini
   2. #2. LangGraph Based
   3. #3. LangGraph (with config.json)
   4. #4. Server Sent Events (SSE)
   5. #5. Streamlit MCP Client
5. Build 3 MCP Servers:
   1. #1 Python STDIO based
   2. #2 Python STDIO (with Docker)
   3. #3 Server Sent Event (SSE) Server
6. Connect your server to Claude Desktop
7. Deploy MCP Server on Google Cloud Platform. Yes, you heard that right 🙂
8. Build Streamlit UI for MCP Client in Python
9. Avoid AI Costs using a free Gemini API Key: to work with Gemini API, Tools and Function calling

Detailed Course Description

Learn basics of MCP

In this hands-on course, you will gain a solid foundation in the Model Context Protocol (MCP) and its bidirectional client-server architecture. We begin by exploring the fundamentals of MCP, equipping you with the conceptual understanding needed to work confidently with this powerful protocol.

Next, you will implement your own MCP server in Python, hosted on a MacBook. This server will be connected to Claude Desktop, showcasing how you can interact with external tools. As part of this quick start, you’ll build and test a tool that executes terminal commands via MCP using Claude Desktop.

Building on this foundation, we’ll guide you through developing your own MCP client in Python, integrating it with Google’s Gemini API. With a free Gemini API Key from Google, you’ll connect your client to your MCP server and perform tool invocations just like Claude Desktop.

By the end of this course, you will:

• Understand the basic architecture and flow of MCP
• Learn to build and run an MCP server using Python
• Connect your custom MCP server to Claude Desktop
• Obtain and configure a free Google Gemini API Key
• Develop your own Python-based MCP client powered by Google Gemini
• Test the interaction between your client and server to run real commands

What This Course Does Not Cover:

• Advanced MCP primitives beyond tool-calling (for now, we focus only on tool invocation)
• Windows-specific setup: While many steps are applicable across platforms, this course uses a MacBook for demonstrations. Windows users may follow along at their discretion.

Important Notes:

• This course uses tools, APIs, Keys and services provided by third-party companies and open source projects. We do not offer any warranties or guarantees related to these services. Learners are responsible for understanding and agreeing to the terms and policies of each provider.
• While many resources exist in fragments across the web, this course brings everything together in a streamlined, tested, and learner-friendly format based on real-world implementations.
• Disclaimer

  The information provided in this course is for educational purposes only.

  Please be advised that when creating a Google account, Google Cloud Platform (GCP) project, using the Gemini API Key or using any other third-party account or service, you are solely responsible for reviewing and understanding the applicable terms and conditions, privacy policies, pricing, usage limits, and any other relevant policies or charges associated with that service.

  The instructor, course provider, and any affiliated parties do not provide any guarantees, warranties, or representations regarding the accuracy, completeness, or current applicability of any third-party services mentioned in this course, including but not limited to Google Cloud’s $300 credit offer and the free Gemini API Key. Policies and offerings may change at any time without notice, and the information in this course may become outdated.

  By proceeding to create and use any such account or service, you agree that you do so at your own discretion and risk. The instructor and related parties shall not be held liable for any losses, charges, damages, liabilities, or consequences arising from your use or attempted use of such services.

  Always exercise independent judgment and due diligence before engaging with any third-party platform or offer. The credits and key from Google are available from Google irrespective of whether you take this course or not. We use them to help students setup and learn about MCP and AI without incurring cloud/AI costs.

Whether you’re a developer, tinkerer, or AI enthusiast, this course will empower you to build practical systems using cutting-edge AI and modern protocol design.