This tutorial was originally published on IBM Developer.

Large language models (LLMs) have transformed how developers build applications, but they face a fundamental limitation: they operate in isolation from the data and tools that make applications truly useful. Whether it's accessing your company's database, reading files from your filesystem, or connecting to APIs, LLMs need a standardized way to interact with external systems.

The Model Context Protocol (MCP) addresses these limitations by providing a standardization layer for AI agents to be context-aware while integrating with the data and tools. Learn more about what MCP is, its client-server architecture components, and its real-world benefits in this “What is MCP?” article or in the MCP docs.

The following figure shows the typical MCP architecture, with MCP hosts, MCP clients, MCP servers, and your own data and tools.

In this comprehensive tutorial, we'll explore MCP and learn how to build a production-ready integration with IBM watsonx.ai, demonstrating how to create AI applications that can seamlessly connect to enterprise data and services.

Continue reading on IBM Developer to learn how to build context-aware AI applications using MCP with Granite models...