Innovative MCPs for ChatGPT and Beyond

**SPEAKER_1** (0:00)
If you want to get access to this episode and my next 30 episodes all ad free, so there'll be no ads on them, go check out my podcast, AI Chat. You can go search for that on Spotify or Apple. It's AI Chat. I'm gonna post all of these news episodes, and I'm also posting interviews, like I just interviewed the CEO of Cohere. They've raised over a billion dollars for their AI model, talking about what they're gonna be spending the money on and the direction of the AI industry, along with all of this new stuff. So if you wanna go check it out with no ads for free, it is AI Chat. Welcome to the podcast. Just yesterday, I released an episode where I said that I have created a tool, an MCP with my company AI Box, that gives Claude the ability to generate images, it gives ChatGPT the ability to generate video once Sora dies and they're gonna have to use Google VO3, and it gives something like Gemini, the ability to generate audio with 11 Labs. So you essentially can take any of the main AI tools you use day to day, and I think Claude, Claude Cowork, Claude Code, is kind of one of the most popular, and you can put all of the other AI models inside of it. Now, I had a lot of people asking me, number one, how MCPs work, what MCPs are, what the difference between an MCP and an API is. So I wanna make an episode where I'm gonna say some of the cool functionality that I've created with AI Box and some of the cool things I've learned on the journey there, but I'm also gonna break down for anyone curious what the top MCPs are that you should be using with Claude or whatever AI tools and assistants you are building with, and how these MCPs and APIs actually work, what the difference is between them, and how you can get the most out of them. So as just two seconds of background, I'm sure most of you know, but just to define the terms and explain what an API is, it's basically the way for one piece of software to talk to another piece of software. And the difference between an AI and an API and an MCP is that an MCP is basically a standard way for an AI assistant, you can think of something like Claude, to basically discover and use tools, any apps, any files that are inside of a database or inside of a service. So an API, a lot of times, not always, it's kind of like, I guess a good analogy for this is kind of a universal adapter, right? So every country in the world has a different outlet plug, that's kind of like an API, it's kind of like a very specific way to use their software. And an MCP is more like a universal adapter that will fit into any of those plugs, because the way an MCP works is it's basically kind of like a document page, it's basically an explanation to something like Claude of all of the ways that your API or all of the ways your software operates and how you can interact with it. Versus like an API was like very structured, very rigid. And as an example from something I've built, I recently built a tool that helped me with posting some of my podcast episodes, but I have three different podcast distribution companies that I use. I use rss.com, I use Acast, I use Art 19
All of them have APIs that help me with my podcast publishing. So a way for me to upload an episode to my platform and have it go and publish it on to one of those distribution networks. But the problem is every single one of those APIs, like the way they work, the way that you integrate them into software is different. So what all of those companies do and what everyone has done with APIs, they've always kind of published a documents page that explains how you structure your API calls, how you have to give them code, how they'll give you code back. And if you have anything wrong in that code that you send them, you get back, it crashes and breaks and the API doesn't work. So it's been pretty, you know, it's frustrating. And a lot of times when I was first building with tools with Claude and building, you know, that particularly that use case, that podcast distribution tool that I created, I would literally say, hey, Claude, here's my API, which I'll tell you why that's a bad thing in the future. But I'd say, here's my API key. And here is a link to their documents page. Read this entire page of context, pages and pages to figure out how the heck their API works and to integrate it. And it still wouldn't work all the time. We'd have to do tons of tests. Sometimes I have to get on with their customer support and then have to look at what I was doing and looking at my code errors and Claude was telling me stuff. And anyways, nightmare. So then we have a beautiful solution, which is an MCP, which is basically they give you a link that you can give Claude that thoroughly helps it understand how your API works and newsflash. MCP is also leveraging an API a lot of the time, but it's basically the universal adapter to explain to the AI agent how to successfully use these APIs as the standardized way to do it.