Скачать или смотреть End the AI Blind Spot! Chrome DevTools MCP + CoPilot, Claude, CLINE Setup Guide

In this video, we’re going to end the AI blind spot once and for all.

I’ll show you how to configure the new Chrome DevTools MCP Server and connect it with your favorite AI clients — including GitHub CoPilot, Claude Desktop, and CLINE.

Once configured, your AI assistant will finally be able to “see” what’s happening inside a real browser — not just rely on curl responses or headless scripts.

💡 What you’ll learn in this video:
1️⃣ How to configure the Chrome DevTools MCP Server step-by-step
2️⃣ How to integrate it with VS Code and AI clients like CoPilot, Claude, and CLINE
3️⃣ Live demo showing the AI assistant accessing browser context — console logs, DOM, network data, and more

Chapters:
0:00 What is Chrome DevTool MCP ?
1:35 How to Setup Chrome Dev Tools MCP ?
2:45 Configure DevTools MCP in VSCode
4:35 Test Chrome DevTools MCP
5:50 Demo of Google Chrome MCP
7:22 Conclusions
7:54 StonerStack Discussion

🧰 Useful Links:
🔗 Chrome DevTools MCP Blog — https://developer.chrome.com/blog/chr...

🔗 GitHub Setup Guide — https://github.com/ChromeDevTools/chr...

⚙️ Tech Covered:

Model Context Protocol (MCP)

Chrome DevTools Protocol

VS Code AI Integrations

GitHub CoPilot, Anthropic Claude, and CLINE setup

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#ChromeDevTools #MCP #VSCode #GitHubCopilot #ClaudeAI #CLINE #AIAssistants #ModelContextProtocol #WebDevelopment #AIDebugging #StonerStack


Description
End the "AI Blind Spot" by giving your AI coding assistant the power of the browser!

This comprehensive guide shows you how to install and configure the Chrome DevTools Model Context Protocol (MCP) Server—the universal open standard that connects your AI to the actual runtime environment.

Whether you use GitHub Copilot, Claude Code, JetBrains IDEs (like CLine), or any other MCP-compatible client, this setup is the single key to unlocking powerful, context-aware debugging.

What Your AI Gains (The "Eyes to See"):

Real-Time Context: Your AI can now access live console messages and network traffic during code execution.

Automated Debugging: Ask your agent to diagnose and fix layout or network issues (e.g., CORS errors) based on live data, not just static code.

Performance Audits: Instruct your AI to run and analyze performance traces to optimize LCP and suggest speed improvements.

Seamless Verification: Automatically verify that AI-suggested code fixes work as expected directly in the browser.

Learn the simple steps for installation via npx and how to add the configuration to your specific AI client to revolutionize your AI-assisted development workflow.

“In this Chrome DevTools MCP tutorial, I’ll show how to connect AI clients like GitHub Copilot, Claude, and CLINE to real browser debugging.”
“This Model Context Protocol server setup lets AI assistants access Chrome’s DevTools data.”