Integrating MCP with LlamaIndex
Learn how to build an MCP client using LlamaIndex that connects and uses tools from a standard MCP server.
In mature software ecosystems, the most valuable components are interchangeable. A specialized tool, like a weather data provider, should not be permanently tethered to a single application or framework. This lesson puts that principle into practice by demonstrating MCP’s core promise: enabling a single, unmodified tool server to be seamlessly consumed by an entirely different agentic framework, proving the value of a standardized communication layer.
The power of interoperability
We have a perfectly functional weather_server that provides a valuable tool to our organization. Now, a different development team wants to leverage this tool for their own project. However, their standard toolkit for building AI agents is LlamaIndex, not LangGraph. They need to integrate our get_weather tool into their new LlamaIndex-based agent, and crucially, they want to do this without requiring our team to make any changes to the weather_server.
This is where the power of a standardized protocol becomes clear. The protocol acts as a strict contract, ensuring that as long as a client can speak MCP, it can use the server’s tools, regardless of the agent framework it is built upon. Our objective in this lesson is to prove this interoperability by building a completely new client application using LlamaIndex. This application connects to and utilizes the exact same weather_server.py from our previous lesson, without a single modification to the server’s code.
To build this new client, we must first understand the framework we will be working with. Let’s introduce LlamaIndex and its specific approach to building agents.
A quick look at LlamaIndex
LlamaIndex is a data framework designed for building applications that connect large language models with external data sources. While it is widely recognized for its powerful retrieval-augmented generation (RAG) capabilities (like ingesting, indexing, and querying data), it also provides a robust toolkit for building intelligent, tool-using agents. For our lesson, ...