Mastering the Model Context Protocol (MCP) on Claude
Field | Description / Template |
|---|---|
Purpose | This course teaches developers how to design, build, deploy, and manage applications using the Model Context Protocol (MCP). Learners will understand MCP architecture, create custom MCP servers and clients, implement secure multi-tool integrations, and build cloud-native AI systems that connect Claude to real-world data sources, APIs, and workflows. |
Audience | AI engineers, software developers, backend engineers, platform engineers, automation developers, GenAI practitioners, and technical architects interested in tool-enabled AI systems and protocol-driven integrations. |
Role | AI Engineer, LLM Engineer, Backend Developer, Platform Engineer, AI Solutions Architect, Integration Engineer, Agentic AI Developer, Infrastructure Engineer. |
Domain | Generative AI, Agentic AI, AI Infrastructure, Protocol Engineering, AI Integration Systems, Cloud-native AI Applications. |
Skill Level | Intermediate to Advanced |
Style | Hands-on, protocol-focused, lab-driven learning with practical server/client implementation, debugging workshops, cloud deployment exercises, and real-world capstone architecture development. |
Duration | 4 Days |
Related Technologies | Model Context Protocol (MCP), Claude Desktop, Python, Node.js, JSON-RPC, StreamableHTTP, STDIO Transport, SQLite, REST APIs, Cloud Deployment, AI Tool Integration, Agentic Systems, OAuth, File Systems, Remote Execution Workflows. |
Course Description
“Mastering the Model Context Protocol (MCP) on Claude” is an advanced engineering course focused on building interoperable AI systems using the Model Context Protocol.
The course introduces MCP as the standardized communication layer between AI models, external tools, APIs, databases, and enterprise systems. Students will learn how to design MCP servers and clients, expose tools and resources to Claude, manage transports, and build scalable multi-server architectures.
Learners will gain practical experience implementing local and remote MCP infrastructures, debugging protocol communication flows, managing security boundaries, and enabling agentic workflows through sampling and tool orchestration.
Through hands-on labs and a production-style capstone project, participants will build cloud-native MCP systems capable of securely connecting AI applications to databases, file systems, APIs, and distributed services.
By the end of the course, students will understand how MCP enables modular, extensible, and maintainable AI ecosystems for enterprise-grade applications.
Who is this course for
Developers and backend engineers building AI assistants with external tool, database, and API integrations
Engineers working with Claude Desktop, MCP-enabled workflows, and protocol-driven AI architectures
AI platform teams, agentic AI developers, and technical architects designing scalable AI ecosystems
Professionals building secure, enterprise-grade AI integrations and reusable AI infrastructure
Course Objectives
By the end of this course, learners will be able to:
Understand the architecture, components, and communication flow of the Model Context Protocol (MCP)
Build and manage MCP Hosts, Clients, and custom MCP Servers with tool and resource integrations
Implement dynamic/static resource access, JSON-RPC messaging, STDIO/HTTP transports, and multi-server workflows
Develop unified AI interfaces with advanced MCP capabilities such as Sampling, Roots, and long-running task handling
Deploy, secure, debug, and monitor scalable cloud-native MCP systems with controlled enterprise access patterns
Prerequisites
Basic to intermediate Python or Node.js programming knowledge
Familiarity with REST APIs and JSON
Understanding of client-server architecture
Basic command-line and development environment experience
Familiarity with AI assistants or LLM concepts is helpful
Course outline
Section 1: Foundations of MCP
What is MCP? Solving the "Integration Spaghetti" Problem
The MCP Ecosystem: Hosts, Clients, and Servers
Lab 1: The Quickstart – Setting up your first MCP environment using pre-built servers (e.g., Google Drive or GitHub) and connecting them to a Claude Desktop client.
Section 2: Building Your First MCP Server
Project Setup: Environment and Dependencies
Defining Tools: Giving Claude "Hands"
The Server Inspector: Debugging your Server in Real-Time
Defining and Accessing Resources (Static vs. Dynamic Data)
Lab 2: The SQLite Server – Build a custom MCP server that exposes a local SQLite database as a set of tools and resources for Claude to query.
Section 3: Developing MCP Clients
Implementing a Custom Client from Scratch
Managing Server Connections and Lifecycles
Defining and Triggering Prompts via the Client
Creating a Unified Interface for Multiple Servers
Lab 3: The Custom Dashboard – Build a basic Python or Node.js application that acts as an MCP Client, connecting to the server you built in Lab 2.
Section 4: Advanced Core Features
Sampling: Allowing Servers to Request Completions from the LLM
Roots: Securing and Defining Filesystem Boundaries
Progress Notifications: Handling Long-Running Tasks
Logging and Debugging Complex Multi-Server Flows
Lab 4: The "Agentic" Server – Enhance your server with Sampling, allowing it to ask Claude to "summarize" a database row before sending the final result back to the user.
Section 5: Transports and Protocol Communication
Under the Hood: JSON-RPC Message Types
The STDIO Transport: Local Communication
StreamableHTTP Transport: Building Cloud-Native MCP Servers
State Management and StreamableHTTP in Depth
Lab 5: Remote Execution – Transition a local STDIO server to an HTTP-based transport, allowing it to be hosted on a cloud provider and accessed remotely.
Section 6: Integration and Review
MCP Review: Security, Scalability, and Best Practices
The Future of MCP and Community Servers
Final Quiz: Comprehensive Model Context Protocol Certification
Capstone Project: "The Universal Workspace"
Create a multi-transport system where Claude can simultaneously read from a local file system (Roots), query a remote database (HTTP), and report progress on a background task.