Get the Best of Both Worlds: How to Use gpt-mcp to Connect Claude with OpenAI's Models
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Zack Saadioui
8/11/2025
Get the Best of Both Worlds: How to Use gpt-mcp to Connect Claude with OpenAI's Models
Hey there. So, let's talk about something that's been a low-key frustration for a lot of us working with AI models. You find a model you absolutely love for certain tasks. Maybe it's Claude's incredible ability to handle long contexts & reason through complex code. You get comfortable with it, you build your workflow around it. But then, there's that one specific tool, that one API, or that new, cutting-edge function that's only available with OpenAI's models.
For the longest time, it felt like you had to pick a side. You were either on Team Claude or Team OpenAI, & you just had to accept the limitations of your chosen ecosystem. It was like having a favorite coffee shop with the best espresso, but they don't have your favorite kind of milk. It’s a classic case of wanting to have your cake & eat it too.
Well, here's the thing. It turns out, you CAN have the best of both worlds. And the key to it all is a pretty cool piece of tech called the Model Context Protocol, or MCP. This isn't just some gimmick; it's a fundamental shift in how we can think about building with AI. It’s the bridge that lets you use your favorite Claude-based environment while still tapping into the massive power & flexibility of OpenAI's models & tools.
Honestly, it's a game-changer. And in this post, I'm going to break down exactly what MCP is, why it matters, & most importantly, show you how you can set up your own
1
gpt-mcp
connection. Let's get into it.
So, What is MCP, Really?
Before we dive into the code, let's get a handle on what MCP actually is. The best analogy I’ve heard is that MCP is like USB-C for AI. Think about it – before USB-C, every device had its own proprietary connector. You had one for your phone, another for your camera, another for your external hard drive. It was a mess of cables & adapters. USB-C came along & standardized everything. One port, one cable, and all your devices can talk to each other.
That's EXACTLY what MCP does for AI models & external tools. For ages, if you wanted an AI model to interact with an external API, say, to check the weather or search a database, you had to build a custom, one-off integration for that specific model. If you wanted to switch the model, you'd often have to rebuild the whole thing. It was brittle, time-consuming, & didn't scale at all.
MCP creates a standardized protocol, an open set of rules, that defines how an AI agent can communicate with a tool. The tool doesn't need to know the intimate details of the AI model, & the AI model doesn't need to know the inner workings of the tool. They just need to speak the same language: MCP.
This system generally has two main parts:
The MCP Client: This is the component that's integrated with your AI model (like Claude). Its job is to take the AI's intent—"I need to know the weather in New York"—and format it into a standardized MCP request.
The MCP Server: This is a separate program that wraps your tool or API. It listens for requests from the MCP client, executes the real-world task (like calling the weather API), & then sends the result back in a standardized MCP format.
This separation is what makes it so powerful. You can have a whole library of MCP servers, each one dedicated to a specific tool. Your AI agent, via the client, can then discover & use any of these tools on the fly without needing to be reprogrammed. It’s a modular, plug-and-play approach to building AI capabilities.
The How-To: A Practical Guide to Connecting GPT & MCP
Alright, theory is great, but let's get our hands dirty. We're going to walk through how to create a simple MCP setup where our client could be in a Claude-like environment, but the tool it uses is actually powered by a GPT model from OpenAI.
For this tutorial, we'll build a simple weather forecasting tool. The full code for this example is available in a fantastic tutorial by BioErrorLog, which I've adapted here.
Step 1: Building the MCP Server
First up, we need our tool server. This part of the code is actually model-agnostic. It doesn't care if Claude, GPT, or any other model is calling it. Its only job is to expose tools that do a specific job. In this case, getting weather data.