Model Context Protocol | MCP | 2025
Understand the Theory behind MCP | Build MCP Servers with FastMCP & Deploy on Google Cloud
Updated on Oct, 2025
Language - English
Lectures -12
Resources -1
Quizzes -1
Duration -1 hours
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Course Description
The AI industry just unified around ONE integration standard - and you need to know it.
In March 2025, OpenAI officially adopted MCP. Google DeepMind followed in April, calling it "rapidly becoming the open standard for the AI agentic era." Over 5,000 MCP servers now exist in the ecosystem, growing from zero in just 6 months. This isn't just another protocol - it's the "USB-C for AI applications" that enterprises like Goldman Sachs, AT&T, and Block are already using in production.
The Problem MCP Solves:
In March 2025, OpenAI officially adopted MCP. Google DeepMind followed in April, calling it "rapidly becoming the open standard for the AI agentic era." Over 5,000 MCP servers now exist in the ecosystem, growing from zero in just 6 months. This isn't just another protocol - it's the "USB-C for AI applications" that enterprises like Goldman Sachs, AT&T, and Block are already using in production.
The Problem MCP Solves:
- Before: "Who are our at-risk customers?" → "I don't have access to your CRM system..."
- After: "Based on your Salesforce data, 5 enterprise customers are at risk this month: Acme Corp has 72% decreased usage, 3 open tickets, renewal in 45 days..."
- MCP Server with FastMCP (Python)
- Real-time log monitoring and customer data integration
- AI-powered support summaries using live business data
- Containerized deployment on Google Cloud with Docker
- Industry Standard: OpenAI, Google, Microsoft Azure all support MCP
- Explosive Growth: 5,000+ servers created in 6 months
- Enterprise Adoption: AWS, Block, Apollo, GitHub using it in production
- Developer Velocity: Zed, Replit, Sourcegraph building on MCP
- Career Future-Proofing: Join the ecosystem before it becomes mandatory
- MCP Protocol Mastery: Architecture, transports
- FastMCP Development: Tools, Resources, Prompts, and Sampling
- Enterprise Integration: Real business data connections
- Production Deployment: Docker, Google Cloud, monitoring
Goals
- Understand the Model Context Protocol (MCP) Architecture
- Learn the core components and interaction model of MCP, including how LLMs, clients, and servers exchange context efficiently.
- Explore the MCP Transport Layer
- Gain a deep understanding of how MCP handles communication between tools and models through standardized transport channels such as WebSocket and JSON-RPC.
- Master the MCP Delivery Mechanism
- Discover how data, prompts, and context are delivered and synchronized across clients, ensuring reliable and scalable context sharing.
- Get Started with FastMCP
- Learn how to quickly implement and test MCP-compliant servers and clients using the FastMCP framework for rapid prototyping.
- Build and Integrate MCP Tools with FastMCP
- Apply the MCP standard to create your own tools that connect to LLMs, expose functionality, and extend model capabilities.
- Leverage MCP Resources in Practical Scenarios
- Learn how to define and manage resources that can be accessed by the model through the MCP interface, such as APIs, databases, and files.
- Design Effective MCP Prompts and Workflows
- Understand how to use MCP Prompts to define dynamic model behavior, improve instruction precision, and integrate real-time context into AI workflows.
- Deploy MCP Applications Using Docker and Google Cloud
- Learn how to containerize your MCP setup, configure environments, and deploy your system for scalable production environments.
Prerequisites
- Basic programming understanding
- Python
- Technical fundamentals (Web, Network)
Curriculum
Check out the detailed breakdown of what’s inside the course
Resources
2 Lectures
-
Motivation
05:46
05:46
-
Resources
Theory
4 Lectures
Hands On - Build your MCP Server
6 Lectures
Instructor Details
Johannes Müller
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