| 1 -10,000 Foot view on Language Models.mp4 | 39.9 MB | ||
| 1 -Agentic Design at Runtime.mp4 | 13.5 MB | ||
| 1 -Agentic Use Case with Multimodal, Multi-Hop and ReAct Architecture.mp4 | 65.6 MB | ||
| 1 -Amazon Bedrock InlineAgent - Intro.mp4 | 11.4 MB | ||
| 1 -Bedrock Agent with Time MCP Server.mp4 | 64.3 MB | ||
| 1 -Current solutions and their limitations - Need for MCP.mp4 | 42.8 MB | ||
| 1 -Github MCP Server on local Docker and Claude Desktop.mp4 | 26.9 MB | ||
| 1 -HandsOn - Streamable HTTP Server.mp4 | 17.8 MB | ||
| 1 -Integrate Claude Desktop with Github.mp4 | 55 MB | ||
| 1 -Introduction to Prompts.mp4 | 13.5 MB | ||
| 1 -MCP Architecture.mp4 | 35.3 MB | ||
| 1 -MCP Documentation.mp4 | 4.6 MB | ||
| 1 -SSE Weather Server.mp4 | 30.5 MB | ||
| 1 -Vector Embedding.mp4 | 30.9 MB | ||
| 2 -Bedrock Agent with Perplexity MCP Server.mp4 | 39.5 MB | ||
| 2 -Client Server Architecture.mp4 | 30.7 MB | ||
| 2 -Inline Agent vs Bedrock Agent.mp4 | 10.5 MB | ||
| 2 -Install Dependencies with UV package.mp4 | 14.5 MB | ||
| 2 -Introduction to CrewAI library.mp4 | 13 MB | ||
| 2 -LLM Inference Parameters.mp4 | 56.2 MB | ||
| 2 -MCP Inspector.mp4 | 8.7 MB | ||
| 2 -MCP Server Components.mp4 | 14.2 MB | ||
| 2 -MCP with Github, Docker, Claude.mp4 | 28 MB | ||
| 2 -Prompting Techniques - Zero Shot, Few Shot, Chain-Of-Thought with Amazon Bedrock.mp4 | 62.5 MB | ||
| 2 -RAG - Retrieval Augment Generation.mp4 | 42 MB | ||
| 2 -ReACT Prompt for AI Agents.mp4 | 48.2 MB | ||
| 2 -SSE Client - Handshake.mp4 | 18.4 MB | ||
| 3 -Cost Analysis Agent - Multi MCP Servers and Builder Tools.mp4 | 115.7 MB | ||
| 3 -First RAG Pipeline.mp4 | 111.8 MB | ||
| 3 -Inline Agent Class Walkthrough.mp4 | 20.5 MB | ||
| 3 -Install CrewAI.mp4 | 26.1 MB | ||
| 3 -MCP Client Server over SSE.mp4 | 17.7 MB | ||
| 3 -MCP Client with HTTP Streamable.mp4 | 20.3 MB | ||
| 3 -MCP Prompts - Hands On.mp4 | 21.6 MB | ||
| 3 -MCP Transport Types.mp4 | 23.8 MB | ||
| 3 -Run the Agent.mp4 | 32 MB | ||
| 3 -Walkthrough Weather API.mp4 | 12.9 MB | ||
| 4 -Amazon Bedrock Agent Console.mp4 | 32.6 MB | ||
| 4 -Cost Analysis Agent - Evaluate Result.mp4 | 41.2 MB | ||
| 4 -Define Agents and Tasks.mp4 | 28 MB | ||
| 4 -Invoke Weather API.mp4 | 27.3 MB | ||
| 4 -MCP Flow - Server, Client and Host communication over Transport layer.mp4 | 17.9 MB | ||
| 4 -MCP Inspector - Client.mp4 | 20.3 MB | ||
| 4 -Multi Agent with Multi Tools.mp4 | 58 MB | ||
| 5 -AWS Profile - CLI.mp4 | 4.2 MB | ||
| 5 -Getting MCP Server Ready.mp4 | 58.4 MB | ||
| 5 -MCP - E2E Flow.mp4 | 25.7 MB | ||
| 5 -MCP Resources - Hands On.mp4 | 19.8 MB | ||
| 5 -Travel Agent Base Classes.mp4 | 28 MB | ||
| 6 -IAM Access Key.mp4 | 11.1 MB | ||
| 6 -Integration - MCP Resource with Claude.mp4 | 16.5 MB | ||
| 6 -MCP Host, Client and Server.mp4 | 35.6 MB | ||
| 6 -Planner Agent with Crewbase.mp4 | 25.3 MB | ||
| 7 -MCP Inspector.mp4 | 25.4 MB | ||
| 7 -MCP Resource - Data Refresh.mp4 | 8.7 MB | ||
| 7 -Multi Agent Execution with Crewbase.mp4 | 22.8 MB | ||
| 8 -Evaluate Multi Agentic Execution.mp4 | 65.2 MB | ||
| 8 -Resource with MCP Inspector.mp4 | 16.1 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 60 total files | |||
MCP Guide: Generative AI with Agents, Model Context Protocol
https://WebToolTip.com
Last updated 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 29m | Size: 1.78 GB
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)
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