Mcp And Acp For Smarter Ai Agents

Learn Model Context Protocol (MCP) and Agent Communication Protocol (ACP) to Build Powerful, Interoperable AI Systems

What you'll learn

Understand the fundamentals of MCP and ACP and their roles in AI systems

Learn to implement MCP for managing model context in AI workflows

Master ACP for enabling efficient communication between AI agents

Build practical projects using MCP and ACP in simulated environments

Explore best practices for designing scalable and secure AI communication systems

Requirements

Basic understanding of AI and machine learning concepts

Familiarity with Python Programming

Knowledge of Networking Basics (e.g., APIs, sockets)

Description

Artificial Intelligence took a giant leap in 2022 with the rise of ChatGPT, bringing powerful Large Language Models (LLMs) into everyday life. But building truly intelligent systems goes far beyond a single AI conversation.That is where Model Context Protocol (MCP) and Agent Communication Protocol (ACP) come in. MCP gives AI the context it needs – the “what” – while ACP enables agents to coordinate and act – the “how”. Together, they form the backbone of agentic AI: systems where AI agents think, decide and work together to solve complex problems.These protocols are not just technical standards; they are enablers of the next generation of AI-driven applications. MCP provides AI models with relevant, real-time information from external sources, ensuring decisions are made with the right context. ACP allows multiple agents, and even different AI systems, to communicate effectively and collaborate on tasks. Combined, they make it possible to build AI ecosystems that are more reliable, scalable and adaptable than ever before.In this course, you will explore both the concepts and the practical skills needed to design and build MCP and ACP-powered systems. We will cover the fundamentals, their role in modern AI architectures, and how they are applied in real-world projects. Leaders, solution architects, product managers and developers will all find value in the lessons. If you are a developer, you will particularly enjoy the hands-on sections, where we implement MCP and ACP using Python and widely used Software Development Kits (SDKs).By the end of the course, you will be able to design and build AI systems that maintain context, coordinate across multiple components, and adapt intelligently to changing requirements. Whether you are building a prototype or scaling an enterprise-grade solution, you will have the knowledge and confidence to leverage MCP and ACP to create innovative, high-performing AI applications.This course is suitable for learners of all proficiency levels and is designed to give you both the strategic understanding and the technical skills to lead the way in AI development.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What is an AI Agent

Lecture 3 What is MCP

Lecture 4 What is ACP

Lecture 5 Why are ACP and MCP Related?

Lecture 6 Demonstration of Weather MCP in Claude Desktop

Section 2: Deep Dive into Model Context Protocol (MCP)

Lecture 7 MCP Architecture : The Host

Lecture 8 MCP Architecture: The Client

Lecture 9 MCP Architecture: The Server

Lecture 10 The Underlaying Protocols of MCP

Lecture 11 MCP in Action with Claude Desktop

Section 3: Coding MCP Servers and Clients for Developers

Lecture 12 Setting Up Your Coding Environment

Lecture 13 Coding an MCP Server Tool with Python

Lecture 14 Coding MCP Server Resource with Python

Lecture 15 Coding MCP Server Prompt with Python

Lecture 16 Loading MCP Server into Claude Desktop

Lecture 17 Streamable HTTP Transport in MCP Servers

Lecture 18 Production-grade Deployment and Stateful MCP Servers

Lecture 19 Developing MCP Clients

Lecture 20 Securing HTTP MCP Servers and Clients with Open Authetnication (OAuth)

Lecture 21 Sampling in MCP

Section 4: Exploring Agent Communication Protocol (ACP)

Lecture 22 About Agent Communication Protocol (ACP)

Lecture 23 Development of ACP

Lecture 24 Agent Run Lifecycle in ACP

Lecture 25 Let's Create an ACP-compatible AI Agent

Lecture 26 Using Large Language Models (LLM) in ACP-compatible Agents

Lecture 27 Agent Manifests and Discovery in ACP

Lecture 28 Messae Structure in ACP

Lecture 29 Stateful ACP Agents

Lecture 30 Integrating MCP Tools with AI Agents and Large Language Models

Section 5: Production Grade ACP Servers

Lecture 31 Introduction

Lecture 32 Scalability of ACP Servers

Lecture 33 Security in ACP Servers

Lecture 34 Implementing Observability in ACP Servers

Section 6: MCP for Leaders and Business Professionals

Lecture 35 Introduction - What is MCP?

Lecture 36 Benefits of Adopting MCP

Lecture 37 Strategies to Implement MCP

Lecture 38 Governance, Security, and Future-Proofing Your MCP Investment

Lecture 39 Assignment: Develop a Framework for Adopting MCP

Section 7: Workshop - SIM Activation Chat Bot

Lecture 40 Introduction

Lecture 41 Preparing the Project Environment

Lecture 42 Developing MCP Tools : Get Number Status and Set Number Status

Lecture 43 Developing SIM Activation AI Agent with ACP

Lecture 44 Securing ACP Agent with OAuth 2.0 and Google

Section 8: Course Wrap-Up

Lecture 45 Ethical Considerations of MCP

AI developers and machine learning engineers,Data scientists interested in AI system integration,System architects designing agent-based AI solutions,Intermediate to advanced learners with basic knowledge of AI, Python, and networking concepts