Mcp Guide: Generative Ai With Agents, Model Context Protocol

Learn MCP ( Model Context Protocol ), AI Agents, Prompt Engineering, Amazon Bedrock, SSE - From Beginner to Expert 2025

What you'll learn

Master Model Context Protocol (MCP) - Understand MCP architecture, server components, transport types, and flow diagrams for enterprise AI communications

Build Production-Ready AI Agents - Create intelligent agents using Claude, CrewAI, and Amazon Bedrock with real-world applications like travel planning and tool

Implement Secure AI Systems - Apply penetration testing methodologies, OAuth authentication, and security best practices specifically for AI agent architectures

Use Docker containerization, SSE transport, streamable HTTP protocols, and multi-server architectures for enterprise deploym

Integrate AI with Modern Development Workflows - Connect AI agents with GitHub, implement CI/CD pipelines, and manage cost-effective cloud-based AI services

Requirements

Basic programming knowledge (any language)

Description

Unlock the future of AI development with the most comprehensive course on Generative AI Agents and Model Context Protocol (MCP) available in 2025. This cutting-edge program combines artificial intelligence, cybersecurity, and modern development practices to make you an industry-ready AI specialist.Why This Course is Essential: The AI industry is rapidly evolving with MCP becoming the new standard for AI communication protocols. Major tech companies are adopting MCP for secure, scalable AI agent interactions. This course positions you at the forefront of this technological revolution.What Makes This Course Unique:Latest MCP Standards: Learn the newest Model Context Protocol implementationsReal-World AI Agents: Build production-ready AI systems using Claude and Amazon BedrockSecurity-First Approach: Integrate penetration testing methodologies with AI developmentIndustry-Standard Tools: Master Docker, SSE transport, OAuth, and modern development workflowsHands-On Projects: Create travel agents, weather APIs, and multi-server architecturesPerfect for:Software developers transitioning to AICybersecurity professionals expanding into AI securityData scientists wanting practical AI implementation skillsTech entrepreneurs building AI-powered productsAnyone serious about AI career advancementCourse Highlights: Master the complete AI development stack from basic concepts to advanced enterprise deployments. You'll start with language model fundamentals and progress through MCP architecture, server components, and transport protocols. Learn to implement secure AI communications using SSE and streamable HTTP transport methods.Build real-world applications including weather APIs, GitHub integrations, and Docker containerization. Develop AI agents using CrewAI and Amazon Bedrock, implementing both inline and console-based agents. Master cost analysis tools and multi-server architectures for enterprise-scale deployments.The course emphasizes security throughout, teaching penetration testing techniques specific to AI systems. You'll learn to identify vulnerabilities in AI agent communications and implement robust security measures using OAuth and advanced authentication protocols.Technical Skills You'll Master:Model Context Protocol (MCP) architecture and implementationAI agent development with Claude, CrewAI, and Amazon BedrockDocker containerization for AI applicationsSSE (Server-Sent Events) and HTTP streaming protocolsOAuth implementation and security best practicesGitHub integration and CI/CD for AI projectsPenetration testing for AI systemsCost optimization for cloud-based AI servicesIndustry Applications: This knowledge directly applies to roles in AI engineering, cybersecurity, DevOps, and full-stack development. Companies worldwide are seeking professionals who understand both AI capabilities and security implications. The MCP protocol knowledge alone positions you for premium consulting opportunities.Hands-On Learning Approach: Every section includes practical exercises, real code implementations, and project-based learning. You'll build a portfolio of AI applications demonstrating your expertise to potential employers or clients.

Overview

Section 1: General Concepts

Lecture 1 10,000 Foot view on Language Models

Lecture 2 LLM Inference Parameters

Section 2: Evolution of MCP

Lecture 3 Current solutions and their limitations - Need for MCP

Lecture 4 Client Server Architecture

Section 3: All About MCP

Lecture 5 MCP Architecture

Lecture 6 MCP Server Components

Lecture 7 MCP Transport Types

Lecture 8 MCP Flow - Server, Client and Host communication over Transport layer

Lecture 9 MCP - E2E Flow

Section 4: Hands On MCP - STDIO Transport with Cursor IDE

Lecture 10 MCP Documentation

Lecture 11 Install Dependencies with UV package

Lecture 12 Walkthrough Weather API

Lecture 13 Invoke Weather API

Lecture 14 Getting MCP Server Ready

Lecture 15 MCP Host, Client and Server

Lecture 16 MCP Inspector

Section 5: Claude with Github using MCP and Github Access Tokens

Lecture 17 Integrate Claude Desktop with Github

Section 6: MCP with Docker

Lecture 18 Github MCP Server on local Docker and Claude Desktop

Lecture 19 MCP with Github, Docker, Claude

Section 7: Hands On MCP - SSE Transport with Cursor IDE

Lecture 20 SSE Weather Server

Lecture 21 SSE Client - Handshake

Lecture 22 MCP Client Server over SSE

Section 8: Hands On MCP - Streamable HTTP Transport

Lecture 23 HandsOn - Streamable HTTP Server

Lecture 24 MCP Inspector

Lecture 25 MCP Client with HTTP Streamable

Section 9: MCP Prompt & Resources

Lecture 26 Introduction to Prompts

Lecture 27 Prompting Techniques - Zero Shot, Few Shot, Chain-Of-Thought with Amazon Bedrock

Lecture 28 MCP Prompts - Hands On

Lecture 29 MCP Inspector - Client

Lecture 30 MCP Resources - Hands On

Lecture 31 Integration - MCP Resource with Claude

Lecture 32 MCP Resource - Data Refresh

Lecture 33 Resource with MCP Inspector

Section 10: Amazon Bedrock Agents - Setup

Lecture 34 Amazon Bedrock InlineAgent - Intro

Lecture 35 Inline Agent vs Bedrock Agent

Lecture 36 Inline Agent Class Walkthrough

Lecture 37 Amazon Bedrock Agent Console

Lecture 38 AWS Profile - CLI

Lecture 39 IAM Access Key

Section 11: MCP Server with Amazon Bedrock Agents

Lecture 40 Bedrock Agent with Time MCP Server

Lecture 41 Bedrock Agent with Perplexity MCP Server

Lecture 42 Cost Analysis Agent - Multi MCP Servers and Builder Tools

Lecture 43 Cost Analysis Agent - Evaluate Result

Section 12: AI Agents

Lecture 44 Agentic Design at Runtime

Lecture 45 Introduction to CrewAI library

Lecture 46 Install CrewAI

Lecture 47 Define Agents and Tasks

Lecture 48 Travel Agent Base Classes

Lecture 49 Planner Agent with Crewbase

Lecture 50 Multi Agent Execution with Crewbase

Lecture 51 Evaluate Multi Agentic Execution

Section 13: Bonus - Multimodal AI Agent with Tools, Multi-Hop and ReAct Prompt

Lecture 52 Agentic Use Case with Multimodal, Multi-Hop and ReAct Architecture

Lecture 53 ReACT Prompt for AI Agents

Lecture 54 Run the Agent

Lecture 55 Multi Agent with Multi Tools

Section 14: RAG - Retrieval Augmentation Generation

Lecture 56 Vector Embedding

Lecture 57 RAG - Retrieval Augment Generation

Lecture 58 First RAG Pipeline

Software Developers wanting to specialize in AI agent development and MCP implementations,Cybersecurity Professionals seeking to understand AI security testing and penetration methodologies,Tech Entrepreneurs building AI-powered products and needing comprehensive technical knowledge,Full-Stack Developers expanding into AI integration and modern protocol implementations,Career Changers serious about entering the high-demand AI engineering field