Add Browser Superpowers To Your Cursor AI workflow Using BrowserTools: A Complete Guide | by Edwin Klesman | Mar, 2025 | Medium


This article explains how to integrate BrowserTools with Cursor AI using the Model Context Protocol (MCP) to enhance AI-assisted web application development and debugging.
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Use MCP to enhance your Cursor AI assistant with browser capabilities

Add Browser Superpowers To Your Cursor AI workflow Using BrowserTools: A Complete Guide

What if your AI assistant could do more than just read and write code? What if it could interact with your browser, debug in real-time, and help optimize your web applications? In this article, you’ll learn what MCP is, how BrowserTools provides browser capabilities, and how to utilize both things to set up a browser debugging integration for your Cursor AI development process.

🗺️ Background

As developers, we’re constantly looking for ways to make our workflow more efficient. With the rise of AI-powered coding assistants, tools like Cursor have become invaluable.

Things have been progressing quickly since late 2024 since AI Agents emerged. The agents reason and implement changes for you. In YOLO mode, you can even lean back and let it do all the workwith minimal interference.

Then Vibe coding came about, letting you use AI powered plugins and/or tools that translate voice into text, and paste that into the AI chats in tools like Cursor AI.

But even with those advancements, developers needed to copy-pasting in browser console and network request errors when coding a web-based application.

How cool would it be if you can let your agent interact with your browser, debug in real-time, and help optimize your web applications? That’s where Model Context Protocol (MCP) and BrowserTools come in.

Whether you’re debugging complex issues, optimizing performance, or improving SEO, the combination of MCP and BrowserTools provides the context and capabilities needed for more effective development.

👩‍🏫 What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is a protocol open-sourced by Anthropic, that standardizes how applications provide context and tools to Large Language Models (LLMs). Think of it as a plugin…

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