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playwright-mcp

MCP.Pizza Chef: qabyai

playwright-mcp is an MCP server that exposes the full browser context of Playwright to AI models, enabling them to accurately understand and interact with web pages. It solves common issues in AI-assisted test writing such as flaky tests, broken scripts, and debugging difficulties by providing real-time, structured visibility into the web page's state and elements. This allows AI assistants to generate precise selectors and robust test scripts, improving automation reliability and developer productivity.

Use This MCP server To

Enable AI to generate accurate Playwright test selectors Provide real-time web page context to AI for test script generation Reduce flaky tests by improving AI understanding of page elements Automate debugging of Playwright tests with AI visibility Integrate AI-assisted test writing into CI/CD pipelines Enhance browser automation workflows with AI-driven insights

README

How to Use playwright-mcp?

npm version Docs

Introduction

Ever tried using Cursor or Claude to write Playwright tests? Yeah, it's kind of a mess.

Why? Because your AI assistant has no clue what's on your web page. It's like trying to describe a painting to someone who's blindfolded. The result?

  • Flaky tests → The AI is guessing selectors, and it gets them wrong.
  • Broken scripts → You spend more time fixing tests than writing them.
  • Debugging nightmares → The AI can't "see" what's happening, so you end up doing all the heavy lifting.

Wouldn't it be nice if your AI could actually understand your web page instead of just making educated guesses?

Enter playwright-mcp !

playwright-mcp gives your AI assistant superpowers by making the browser fully visible to it. Instead of working in the dark, your AI assistant now has real-time access to the page DOM, elements, and interactions.

How playwright-mcp Works (AKA How We Fix This Mess)

Once installed, playwright-mcp spins up a Playwright-controlled browser and gives your AI assistant full access to it. This unlocks:

  1. Recording interactions → Click, type, scroll—let playwright-mcp turn your actions into fully functional Playwright test cases.
  2. Taking screenshots → Capture elements or full pages so your AI gets actual visual context (no more guessing).
  3. Extracting the DOM → Grab the entire HTML structure so the AI can generate rock-solid selectors.
  4. Executing JavaScript → Run custom JS inside the browser for debugging, automation, or just for fun.

Why You'll Love playwright-mcp

🚀 AI-generated tests that actually work → No more flaky selectors, broken tests, or guesswork.

⏳ Massive time savings → Write and debug Playwright tests 5x faster.

🛠️ Fewer headaches → Your AI assistant gets live page context, so it can generate real test cases.

🔌 Works out of the box → Plug it into Cursor, Claude, WindSurf, or whatever you use—it just works.

playwright-mcp FAQ

How does playwright-mcp improve AI-generated Playwright tests?
It exposes the full browser context to AI, allowing precise selector identification and reducing flaky tests.
Can playwright-mcp be integrated with existing Playwright test suites?
Yes, it works alongside existing Playwright setups to enhance AI interaction without disrupting workflows.
Does playwright-mcp support real-time updates of the web page state?
Yes, it provides real-time, structured visibility into the page, enabling dynamic AI responses.
Is playwright-mcp compatible with multiple LLM providers?
Yes, it is provider-agnostic and works with OpenAI, Claude, Gemini, and others.
What environments does playwright-mcp support?
It supports all environments where Playwright runs, including local, CI, and cloud environments.
How secure is the data exposed by playwright-mcp?
It follows MCP principles for secure, scoped, and observable model interaction to protect sensitive data.
Can playwright-mcp help with debugging test failures?
Yes, by giving AI visibility into the browser state, it helps diagnose and fix test issues faster.
How do I get started with playwright-mcp?
Installation and usage instructions are available in the official docs linked on the npm page.