MCP with LangChain

Learn MCP and build your own server and client with LangChain MCP Adapters

Photo by Vitaly Gariev on Unsplash

Model context protocol (MCP) has been trending right now. It is nothing but an awesome tool that allows you to build agents and use pre-built tools. Also, it helps you connect AI models to different data sources.

In this blog, we’ll first dive into MCP and then cover how to build your server and client with LangChain MCP adapters.

You can also use the Claude desktop as a client. However, I’ll show you how to create your own client.

Let’s get started with what MCP is.

What is MCP?

You can use MCP with pre-built tools and agents. (Image by author)

As you know, large models need to integrate with data and tools. This is where MCP comes into play. MCP allows you to build complex workflows on top of large models.

You can directly use LLMs to get an output. However, this way is not recommended because models may hallucinate.

Large models can work with tools, but this can cause hallucinations. (Image by author)

On the other hand, you can leverage AI tools to get the correct answer. But, this way, it can be a nightmare sometimes. Because it is a difficult task to write custom code for each tool.

This is where MCP plays a key role.

MCP is a standard way to connect large models to different data sources and AI tools. You can think of it as a layer between large models and tools.

MCP is a layer that allows you to connect agents and tools. (Image by author)

You don’t need to create custom tools from scratch. Instead, you first build an MCP and then it can connect agents and AI-powered tools.

Cool right?

You can create your own server to connect to a client. Also, you can build your own client that can interact with all MCP servers.