Building AI Integrations with Model Context Protocol (MCP)

Introduction

This short course explains the Model Context Protocol (MCP) and shows how it solves the AI integration problem. It is for AI engineers, platform and infrastructure teams, developers integrating LLMs with tools, and technical leaders who want a clear architecture. You will learn MCP fundamentals, how MCP models and manages context, the roles in an MCP system, and practical steps to build and test MCP servers and clients with FastMCP.

Quick Facts

• Language: English

What you’ll learn

• Explain the M × N integration problem and why MCP exists.
• Define and manage context using tools, resources, prompts, and sampling strategies.
• Describe roles of MCP hosts, clients, and servers and their responsibilities.
• Trace MCP communication from user request to server execution and back.
• Build and test MCP servers and clients using FastMCP.
• Implement JSON-RPC 2.0 messages and streamable HTTP transport for MCP.
• Integrate MCP servers with modern AI tools such as Claude Code and Cursor.

Who this is for

• AI engineers and developers building agentic systems.
• Platform and infrastructure engineers evaluating MCP as a standard.
• Developers integrating LLMs with tools, data sources, or internal systems.
• Technical leaders seeking a clear architectural understanding of MCP.

Prerequisites

• No prior MCP experience required.

Course Overview

This course starts from first principles and shows why MCP was created: to turn a combinatorial M × N integration problem (many models × many tools) into a composable architecture. You will build a mental model of MCP’s core concepts — hosts, clients, servers, and the way context is expressed — before writing code. The hands-on portion covers building MCP servers and clients using FastMCP, sending and receiving JSON-RPC 2.0 messages over streamable HTTP, and connecting MCP servers to tools like Claude Code and Cursor. The emphasis is on architectural correctness and real-world applicability so solutions scale across tools, models, and environments.

Syllabus Highlights

• Overview: The M × N integration problem and MCP’s goals.
• Context model: Tools, resources, prompts, sampling, and state.
• Architecture: Hosts, clients, servers, and communication flow.
• Hands-on: Building MCP servers and clients with FastMCP.
• Transport and protocol: JSON-RPC 2.0 and streamable HTTP.
• Integrations: Claude Code, Cursor, and common tool adapters.

How MCP works and why it matters

MCP reframes the integration challenge. Instead of writing point-to-point adapters for every model-tool pair, MCP standardises how an agent describes context and calls out to capabilities. Context is defined through tools (APIs or functions), resources (data or priors), prompts (templates and instructions), and sampling rules (how responses are generated). The protocol keeps responsibility boundaries clear so models can request actions without embedding tool logic.

Roles are simple: hosts expose capabilities, servers implement and run tools or data access, and clients (agents or orchestrators) request operations and manage context. Communication is a clear flow: the user or agent issues a request, the client packages context and intent, the server executes and streams results via JSON-RPC 2.0 over HTTP, and the client merges results back into the agent’s context.

Hands-on: Building, testing, and integration

FastMCP gives a lightweight path to implement MCP endpoints and clients. Typical steps: define your tool schema and prompts, implement server handlers that follow JSON-RPC 2.0, expose a streamable HTTP endpoint for real-time responses, and write client code to sample and merge outputs. Testing covers unit tests for handlers, integration tests that exercise streaming transport, and end-to-end checks with a model or simulator.

JSON-RPC 2.0 provides a simple message envelope for requests, responses, and notifications. Streamable HTTP transport lets servers push partial outputs so agents can react incrementally. When integrating with Claude Code or Cursor, map their invocation and streaming primitives to MCP calls — treat them as model endpoints or tool adapters that the MCP server can call or be called from.

How to Enroll / Claim Free Access

1. Locate the course page on the provider platform (platform name: Not specified).
2. Check the price at checkout; confirm any coupon or limited-time offer during enrollment.
3. Enroll and follow the course materials and code repos listed in the syllabus.

Free status can change anytime. Please verify the price on the enrollment/checkout page.

Tips to Complete Faster

• Plan four focused sessions: theory, context model, FastMCP build, and integrations/testing.
• Start with small tools — one server handler and one client flow — before scaling up.
• Use the streaming transport early to observe incremental outputs and debug faster.
• Run integration tests with mocked model responses before connecting live models.

FAQ

• Is it really free? Free status can change; verify the price at checkout before enrolling.
• Will I get a certificate? Not specified.
• How long will it stay free? It can change; offers and discounts are not permanent.
• Do I need prior MCP experience? No prior MCP experience is required.
• What tools will I use? The course covers FastMCP and shows integrations with Claude Code and Cursor.

Conclusion

MCP is a practical standard to make AI integrations manageable and scalable. By focusing on a clear context model, defined roles, and robust transport (JSON-RPC 2.0 + streamable HTTP), MCP reduces duplicated work and speeds up integration with models and tools. The hands-on parts teach how to build and test servers and clients and connect them to modern AI tools. Verify any free offer on the checkout page before enrolling.

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