I'm going to show you a library that lets you communicate with your MCP servers directly in your code. You can use it with any LLM you prefer. Previously, communicating with MCP servers required specific MCP clients. We already have clients like Windinsurf, Cursor, and Claude Desktop. Now, you can use this new MCP client library. It works by using an agent and comes with some pretty cool features. It's also really easy to install. I'll show you how to set it up. If you're not familiar with code, keep in mind that this does require some coding knowledge to use properly. Even if you're new to it, that's not a big issue. I'll also show you how to vibe code with it. Let's get into the video. Let's look at the installation of the library. It's a Python-based library. So, the first step is to check whether Python is installed on your system. Then, you'll create a virtual environment. Once it's created, you need to activate it. The commands for both Windows and Mac OS are shown here, and I'll paste them in the description below as well. If you don't know how to code, you can also get these commands from chatgpt. These are the commands to install the library. They're available on the GitHub repo, too. If you're using Python version 3, which you'll know from the Python version command mentioned earlier, then make sure to run everything using pip 3, not pip, but pip 3. If you're planning to use an open AI key or model to interact with your MCPS, you need to install Langchain OpenAI. For anthropic, you need to install Langchain Anthropic. For other providers like Grock or Llama, you can find the required libraries listed at this link. So once everything is installed, open up your terminal. You can see that I'm in a Python environment. I've installed the pip packages. And now I'll show you how to move forward. First open this directory in cursor using this command. This will launch your project directly in cursor. Once it opens in cursor, create a new file named env. Your project will only have the virtual environment folder. At this point, you need to create the other files yourself and add the code manually. Create the env file and paste the following line with your API key. You only need to paste the key for the provider you're using. I'm using OpenAI in this example. So, I pasted that key. Let me quickly explain the code and how it works. At the top, you can see different inputs from MCPUs, Langchain, and OpenAI. As we're using OpenAI for the LLM, we define a function and load.env env loads the environment variables from the ENV file. Then we create an MCP client using the MCP configuration from a separate file. I've placed it here. You can see the Airbnb MCP configuration is right here. Next, we define the LLM we want to use. If you're using Anthropic, the setup will be a bit different. Then we choose our model and create an agent. This agent takes the LLM, the client we created, and defines the maximum number of steps it can take. It also gives a prompt to the LLM which is used on the MCP server. It then prints the result and gives it to us. This is a very basic example of using the Airbnb MCP. You can modify it however you like and build really interesting applications. You don't need a separate client anymore. You can bind an LLM to an MCP and create modular applications. If you've seen our WhatsApp MCP video, that same concept can be used here to make fully autonomous WhatsApp agents. Now, let me run it for you. The server has started and it's running. It looks like there was some kind of error, but we still got the output. We received the listings from the Airbnb and MCP. It gave us the links because we added a feature that filters listings by our preferences, like having a pool and good ratings. It handpicked based on those conditions. This is a cool implementation. It works, and the possibilities for creating different agents are endless. The code you just saw is already in the GitHub repository, so there's no need to include it in the description. If you want to modify the code, you can either write it yourself or ask Cursor to do it. One issue you might run into is that Cursor doesn't have the context of this framework. To give it that context, scroll down to the features section and go to docs. Add a new doc and in the link field, go back to the GitHub repo and open the readme file. You don't need to provide the link to the entire repository. Just use the readme file since it contains the full documentation. Copy the link and paste it into the doc section. Cursor will read it, index it, and use it as context. To use it in code, type the at sign, go into docs, and select MCP use docs. It will reference that and generate code based on the framework properly. Another thing you can do is convert the repo into an LLM ingestible format if you have any questions about it. To do that, you can replace hub with ingest in the URL. This will open the repository and get ingest. It will convert the entire repo into readable text that you can use with any LLM. You can then ask questions about it if you're ever confused or need clarification. You've seen it in action and you can check the repo for other example use cases like using Playright and Airbnb. I use the Airbnb one but with OpenAI. The Blender MCP server can also be used. This framework also supports HTTP connections which means you can connect to servers running on local host. It includes multi-server support too, allowing multiple servers to be defined in a single file. If you're working with multiple MCP servers, you can either specify which result should come from which server or handle it dynamically. By setting use service manager to true, the agent will intelligently choose the right MCP server. You can also control which tools it has access to. This is a solid framework and I'm already thinking of all the wild ways to build new applications with the MCP library. You should check it out too. I'm working on a few projects with it right now. If you don't fully understand it, I've already shown how you can use an LLM to make sense of everything. You can also ask chat GPT or let cursor write the code for you. If you like the video, consider donating through the link in the description and do subscribe. Thanks for watching.