A tool that connects AI coding assistants to Chrome's DevTools, enabling them to see, interact with, and debug live web applications in real-time.
What is Chrome DevTools MCP?
Chrome DevTools MCP is a bridge that connects AI coding assistants to a live Chrome browser through the Model Context Protocol (MCP). It solves a fundamental problem: AI coding tools have been "programming with a blindfold on" because they couldn't see or interact with the code they wrote. This tool removes that blindfold by giving AI assistants the ability to run code in a real browser, inspect what's happening, and fix issues based on actual feedback.
Understanding the Model Context Protocol
MCP is an open standard introduced by Anthropic in late 2024 that acts as a universal adapter for connecting AI models to external tools and data sources. Instead of building custom integrations for each tool, developers can use MCP servers that expose functionality in a standardized format. AI clients can then connect to any of these servers through the MCP interface, enabling context-aware AI that can retrieve information and interact with real systems.
How It Works
Chrome DevTools MCP operates through several key technologies:
- Chrome DevTools Protocol (CDP): The native debugger interface that controls the browser at a low level
- Puppeteer: A battle-tested automation library that handles browser control reliably, managing details like page loads and DOM readiness
- MCP Protocol Layer: Wraps browser capabilities behind a standardized interface, translating high-level AI commands into browser actions
- Local Server: Runs on your machine as an npm package, launching Chrome instances (headless or visible) as needed
- Security & Isolation: Uses separate user data directories to keep AI browsing isolated from your personal browser profile
Core Capabilities
Chrome DevTools MCP provides AI assistants with a comprehensive toolkit:
Performance Analysis: Record performance traces, extract metrics like Largest Contentful Paint, and run automated audits similar to Lighthouse
Page Navigation: Open pages, manage tabs, navigate URLs, and control browser history
User Interaction: Click elements, fill forms, handle dialogs, upload files, and simulate complex user flows
DOM Inspection: Execute JavaScript, read console logs, capture DOM snapshots, and take screenshots
Network Monitoring: List network requests, examine responses, and diagnose issues like missing resources or CORS errors
Browser Emulation: Throttle CPU, adjust network speed, and resize viewports to test under different conditions
Practical Use Cases
Real-Time Code Verification: AI can immediately test its code changes in the browser to confirm they work as expected, catching mistakes before they reach production.
Error Diagnosis: Instead of guessing about issues, AI can examine console logs and network requests to identify the actual cause of problems like failed image loads or broken API calls.
User Flow Testing: AI can reproduce complex user interactions to hunt down bugs that only appear during specific sequences of actions.
Layout Debugging: AI can inspect live DOM and CSS to diagnose styling issues, identifying problems like overflow or misalignment based on the actual rendered state.
Performance Optimization: AI can run full performance traces, measure real metrics, and suggest targeted optimizations based on data rather than guesswork.
Creative Automation: Beyond debugging, AI can automate web tasks like downloading research papers from Google Scholar or validating SEO tags across pages.
Getting Started
Setting up Chrome DevTools MCP is straightforward:
- Ensure you have Node.js 22+ and Chrome installed
- Configure your AI tool to launch the server via npx
- Add a simple configuration entry pointing to
chrome-devtools-mcp@latest
The tool works with popular AI coding assistants including Cursor, Claude Code, Gemini CLI, and Cline.
The Future of AI-Assisted Development
Chrome DevTools MCP is currently in public preview, with new features being added incrementally based on community feedback. The project is open-source and welcomes contributions on GitHub. This tool represents a shift from AI as an autocomplete engine to AI as a true co-pilot that can interact with applications the way human developers do—not just writing code, but actively debugging, testing, and optimizing alongside you.