Using MCPs to Optimize Claude Performance

Jaden Schaefer· Host0:00

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 Veo 3. And it gives something like Gemini the ability to generate audio with ElevenLabs. 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, uh, 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, uh... I guess a good analogy for this is y- kind of a universal