MCP & A2A - Model Context Protocol & Agent to Agent Protocol
Build MCP Clients, Servers | Learn A2A AgentToAgent Protocol | FREE Gemini Key | Python, Gemini, LangGraph, MacOS
Development ,Data Science,Python
Lectures -29
Duration -1.5 hours
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Course Description
TLDR - In this course, you'll learn about MCP & A2A - Model Context Protocol & Agent2Agent Protocol. We will build MCP Clients and MCP Servers from Scratch. 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 Servers and Clients and use Google's ADK or Agent Development Kit
Key Objectives of Course
- Build A2A Servers, Clients (using Google's ADK or agent development kit) - in Beta
- Build MCP Clients:
- #1. Basic Python + Gemini
- #2. LangGraph Based
- #3. LangGraph (with config.json)
- Build MCP Servers:
- #1 Python STDIO based
- #2 Python STDIO (with Docker)
- Connect your server to Claude Desktop
- 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
- 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.
- 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.
Goals
- You'll build MCP Clients and MCP Servers from Scratch. Full Working Code Included!
- Learn Agent to Agent (A2A) Protocol Lifecyle and Components and how this compares with MCP
- We'll use a free Gemini API Key, so you don't need to pay for AI Models when learning!
- We'll use Python, Gemini, LangGraph on MacOS
Prerequisites
Code WalkThroughs Included, no in-depth experience neededBasic Python is a must
Basic familiarity with LLMs like Google Gemini/Claude
Course Recorded using MacOS. Windows Users can follow at their discretion

Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
MCP Overview - What is Model Context Protocol? 04:36 04:36
Build Your Own MCP Server
6 Lectures

Build Your Own MCP Client (Using Python + Google Gemini API)
5 Lectures

Build Docker MCP Server
1 Lectures

Build LangChain MCP Client
1 Lectures

Build MCP Client with Multiple Server Support
7 Lectures

A2A or Agent to Agent Protocol - Lifecycle and Core Components
7 Lectures

Building your own A2A Client and A2A Server
1 Lectures

Instructor Details

Kartik Marwah
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