The Model Context Protocol (MCP) establishes a standard interface for connecting AI assistants to data systems. This course covers the architectural definitions of the protocol, enabling developers to build servers that expose local and remote resources to Large Language Models (LLMs). We examine the primary primitives of MCP: Resources, Prompts, and Tools, and implement them using the official SDKs. You will learn to configure transports, handle JSON-RPC message flows, and integrate custom servers with MCP-compliant clients like Claude Desktop. The content focuses on the technical specification and implementation details required to construct reliable context providers.
Prerequisites Intermediate Python knowledge
Level:
MCP Architecture
Understand the Client-Host-Server relationship and the JSON-RPC message flow that underpins the protocol.
Server Implementation
Build functional MCP servers that expose static and dynamic data through Resources.
Tool Creation
Develop executable Tools that allow LLMs to perform actions and retrieve data from external APIs.
Client Integration
Configure and debug connections between custom MCP servers and standard clients like Claude Desktop.
© 2026 ApX Machine LearningEngineered with