MCP (Modo Context Protocol) is a game-changing technology that has revolutionized the way we approach AI coding. By providing a universal interface to connect AI models with various data sources and external systems, MCP has transformed the landscape of AI-powered development.

One of the key advantages of MCP is its ability to seamlessly integrate with your existing workflow. With MCP, your AI coding assistant can now access a wide range of functionalities, from generating game assets to debugging your applications. This level of integration not only boosts your productivity but also unlocks new possibilities for your projects.

Leveraging the power of MCP, you can now:

• Generate Game Assets: Integrate your AI coding assistant with text-to-image models to automatically create game assets, such as backgrounds, characters, and more, directly within your development environment.

• Debug Applications: Connect your AI coding assistant to browser tools, allowing it to access console logs, network requests, and even select specific elements for modification, streamlining the debugging process.

• Connect to External Services: Build custom MCP servers to integrate your AI coding assistant with any external API or service, expanding its capabilities and enabling it to perform a wide range of tasks.

Setting up your own MCP server is surprisingly straightforward, thanks to platforms like Cloudflare Workers. By following a few simple steps, you can quickly create and deploy your own MCP functions, tailoring them to your specific needs.

Explore the vast ecosystem of pre-built MCP integrations, and discover how you can leverage these powerful tools to transform your AI coding workflow. Join the AI Builder Club community to access a curated list of high-quality MCP resources and connect with like-minded developers who are pushing the boundaries of AI-powered development.

Embrace the power of MCP and unlock a new level of productivity and creativity in your AI coding journey.

There are several marketplaces that offer a variety of pre-built MCP (Modo Context Protocol) solutions. These platforms allow you to search for and integrate existing MCPs into your AI coding workflow, boosting your productivity.

Some popular MCP marketplaces include:

1. GL.A SMI Cursor Directory: This directory provides a list of MCPs created by the community, which you can easily integrate into your AI coding IDE.

2. ASM Systems: This platform hosts MCP servers that you can utilize in your projects. You can search for the MCP you need and copy the command line to add it to your AI coding environment.

When integrating pre-built MCPs, keep in mind that the quality and stability of these solutions can vary. Some may not work as expected, and the documentation may be incomplete. To address this, you can explore the community-maintained list of high-quality and usable MCPs in the AI Builder Club, which I will be sharing.

To use a pre-built MCP, follow these general steps:

1. Identify the MCP you want to use and copy the provided command line.
2. In your AI coding IDE, navigate to the MCP server settings and click "Add MCP".
3. Provide a name for the MCP and paste the command line you copied.
4. Click "Add" to integrate the MCP into your workflow.

Once the MCP is set up, you can start using it in your AI coding tasks, such as generating code, debugging, or accessing external resources.

Remember, while pre-built MCPs can be a valuable resource, building your own custom MCPs can also be a powerful way to extend the capabilities of your AI coding agents. I'll cover the process of creating your own MCP server in the next section.

Building custom MCP (Modo Context Protocol) servers is a powerful way to extend the capabilities of your AI coding assistant, such as Cursor. By creating your own MCP servers, you can connect your agent to a wide range of external services, data sources, and tools, unlocking new possibilities for your workflow and productivity.

One of the key advantages of custom MCP servers is the ability to integrate your agent with any service or system you need. For example, you can create an MCP server that allows your agent to call a text-to-image model, generating custom assets for your game development projects. You can also build MCP servers that provide access to your browser's console and network tabs, enabling your agent to debug your applications more effectively.

Setting up a custom MCP server may seem daunting at first, but the process is actually quite straightforward. By leveraging cloud platforms like Cloudflare Workers, you can quickly create and deploy your own MCP server with minimal effort. The example provided in the transcript demonstrates how to set up a simple "say hello" MCP server using Cloudflare Workers and TypeScript.

Once you have your custom MCP server set up, you can easily integrate it into your Cursor workflow. Simply add the MCP server to your Cursor settings, and your agent will be able to access the functionality you've built.

To help you get started, the presenter has shared a list of high-quality MCP servers that they have found to be extremely useful for AI coding. You can access this list by joining the AI Builder Club community, which the presenter has mentioned in the transcript.

By building your own custom MCP servers, you can unlock the full potential of your AI coding assistant and take your productivity to new heights. Embrace the power of MCP and let your creativity soar!

The Browser Tools MCP is a powerful integration that allows your AI coding assistant to directly access your browser's console logs and network tabs. This integration significantly streamlines the debugging process, enabling your assistant to run app debugging tasks and communicate with you about specific elements you want to change on the UI.

To set up the Browser Tools MCP, you'll first need to clone the Chrome plugin from the provided GitHub repository. Once cloned, you can click on the settings in your Chrome extension and load the compiled Chrome extension folder. Next, you'll add the MCP server to your AI coding assistant by providing a name (e.g., "Browser Tools") and selecting the "command" type, then pasting the provided command line.

With the Browser Tools MCP set up, you can now easily interact with your assistant to access console logs, network requests, and even select specific elements on the page. For example, you can ask your assistant, "What's in my console log?" and it will retrieve and display the console logs for you. Additionally, you can select an element on the page, and your assistant can update the style of that element based on your instructions.

This integration significantly enhances your debugging capabilities, allowing you to seamlessly troubleshoot issues and make UI changes directly from your AI coding assistant. By bridging the gap between your coding environment and the browser, the Browser Tools MCP streamlines your workflow and boosts your productivity.

One of the most powerful features of MCP (Modo Context Protocol) is its ability to connect your AI coding agent to external services and resources. In this section, I'll demonstrate how you can create a custom MCP server to integrate a text-to-image model, allowing your cursor to automatically generate game assets on demand.

First, I'll show you how to set up a Cloudflare worker to host your custom MCP server. This provides a simple and hassle-free way to create and deploy your own MCP functions without the need for complex server setup.

To get started, I'll create a new Cloudflare project using the command line:

npm create cloudflare@latest

I'll name the project "my-mcp" and choose the "Hello World" example, which includes a basic TypeScript setup.

Next, I'll navigate to the project directory and add the necessary code to integrate a text-to-image model, such as the one provided by Replicate. I'll create a new function called "generate-image" that takes a prompt and size as input, and returns the URL of the generated image.

Once the function is defined, I'll update the MCP command line that I can use within my AI coding agent. This command will include the Cloudflare worker URL and the specific function I want to call.

Now, when I'm working on a game project and need to generate new assets, I can simply ask my AI agent to "generate an image of a dog" or any other prompt. The agent will then call the custom MCP server, which will in turn leverage the text-to-image model to create the desired image and return the URL.

This integration allows me to seamlessly incorporate image generation into my AI-powered coding workflow, boosting my productivity and creativity. By leveraging the power of MCP, I can easily connect my AI agent to a wide range of external services and resources, unlocking new possibilities for automating and enhancing my development process.

In this video, we have explored the power of the mCP (Modo Context Protocol) integration into AI coding IDEs. The mCP provides a universal interface for connecting AI models to various data sources and external systems, revolutionizing the way we approach AI-powered coding.

We have discussed the benefits of using mCP, including the ability to access browser console logs, network requests, and even interact with specific elements on a web page. Additionally, we have demonstrated how to create custom mCP servers to connect to external services, such as text-to-image models, to enhance the capabilities of our AI coding agents.

While setting up mCP servers may initially seem daunting, we have shown that it can be done relatively easily using cloud-based platforms like Cloudflare Workers. By creating our own mCP functions, we can tailor the integration to our specific needs and boost our productivity in the AI coding realm.

Furthermore, we have highlighted the challenges of using existing mCP resources, as many of them may not work out of the box or have incomplete documentation. To address this, we have shared our own curated list of high-quality mCP resources in the AI Builder Club community, which you can access by clicking the link in the description below.

In conclusion, the integration of mCP into AI coding IDEs has the potential to revolutionize the way we approach software development. By leveraging the power of this protocol, we can unlock new possibilities and streamline our workflows, ultimately leading to increased productivity and more efficient coding practices.