Getting Started with Playwright MCP

If you’ve been keeping an eye on AI tooling, you’ve probably seen how Model Context Protocols (MCPs) let LLMs talk to different APIs, query databases, and read files. Simply put, they do things that make them far more useful than a simple chatbot.

For the past couple of years, we've been using AI tools like Claude, GPT-4/5, and Copilot to generate code. However, the problem was that they couldn’t execute it. MCPs have changed that by acting as a universal bridge, allowing AI models to connect directly to local servers and tools.

In the testing world, Playwright MCP is a great example of this shift. This MCP allows AI models to connect with browsers directly and create tests for you using Playwright.

This article will walk you through what Playwright MCP actually is, how you can set it up, why it demands senior-level skills, and when managed AI QA tools like QA.tech might make more sense.

What Is the Playwright MCP Server?

The Playwright MCP server is a model context protocol server that provides browser automation features using Playwright. It’s basically a translator between your LLM and browser actions.

Playwright MCP provides two key modes:

• Snapshot mode (the default mode using the accessibility tree)
• Vision mode (a fallback when accessibility fails)

In the past, if you wanted a Claude code to interact with a database, you had to write a custom script for every tool. Now, MCP standardizes that. You run a local server called the MCP server, which exposes a defined set of capabilities, and the AI client knows when to use them.

The Playwright MCP architecture is simple:

AI model → MCP client → Playwright MCP server → Browser

Playwright MCP exposes actions like browser_navigate, browser_click, browser_snapshot, and more as MCP tools that AI models can call directly. As a result, LLMs can interact with web pages without the need for vision models.

The real use cases are numerous, such as rapid prototyping for UI workflows, ad-hoc automation, test generation, automated form submissions, and much more.

Still, you may be confused about the differences between regular Playwright and Playwright MCP, so let’s clear that up. With the standard version, you’re in the driver’s seat, defining every turn; with Playwright MCP, on the other hand, AI is the driver. All you need to do is specify your destination.

But MCP doesn’t solve everything out of the box. Authentication complexity remains a problem, and multi-factor auth, OAuth flows, and managing sessions are still not automatic. In addition, flaky tests don’t just disappear with AI running things, and dynamic content, such as shadow DOM, iframes, and lazy-loaded elements, is still difficult to handle.

Setting up Playwright MCP Locally

Now, let’s talk about setup. Fair warning: this isn’t a simple “just run this command” situation. It requires some environment configuration.

You’ll need Node.js 18 or newer installed on your computer, along with an MCP client, such as Claude Desktop, VS Code, or Cursor. You’ll also need to install browser binaries for any of your favorite browsers, like Chrome or Firefox.

Let’s assume you’ve already downloaded Claude Desktop and you need to connect to Playwright MCP. Where do you start?

First, configuration requires MCP clients to use JSON config files to know which servers to connect to. On Mac/Linux, that’s ~/Library/Application Support/Claude/claude_desktop_config.json. On Windows, it’s %APPDATA%\\Claude\\claude_desktop_config.json. You can find more details about the MCP configuration here.

You need to add:

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": [
        "-y",
        "@playwright/mcp@latest"
      ]
    }
  }
}

Restart Claude Desktop, and you’re ready to test. Prompt something like: “Go to QA.tech and take a screenshot of the page.”

If everything works, you will see the model in the process of thinking, making a call to Playwright to control the browser that has no UI, and then providing you with an image. When things go wrong, you’ll likely run into connection timeout errors, incompatible Node.js versions, downloads failing due to network blockage, or JSON config errors (the most common and hardest to debug).

If any of these issues pop up, you are going to waste so much time debugging them with no feedback. You’ll have no information to determine if the path is incorrect, if there is an error in the syntax, or if there was no attempt to load the file. In short, you’ll end up commenting out lines in the code until something works.

Why Does Playwright MCP Demand Senior-Level Skills?

Playwright MCP is far from bad, but it’s not really for everyone. It requires you to have solid expertise working with LLMs and sometimes distributed systems.

In my experience, the main challenges are:

1. Context management problem: MCP servers are stateless, which means every request starts fresh. The AI doesn’t remember what you asked 5 seconds ago, and you need to start everything from scratch. There’s no built-in session memory for managing the state.
2. Prompt engineering: In a custom MCP server, you’re not just writing instructions. You are learning how to constrain LLM behavior so as not to waste tokens. In the LLM world, if your prompts are not well-crafted, you’ll burn through your tokens without building much.
3. Zero CI/CD: A custom Playwright MCP server runs locally, and this can be a major blocker for teams. If you want to do this as a GitHub action, you will need to containerize the MCP server using Docker, handle authentication, manage API tokens (to keep token usage as low as possible), and maybe even build a custom dashboard to see the outputs.
4. Test flakiness and hallucinations: Tests created by artificial intelligence have limitations. For instance, if a model has previously encountered a specific component of an application, it may still attempt to use it even though it’s no longer available, rather than learning again how to interact with the current state of your app.

Playwright MCP Server vs. Managed AI QA

At this point, you have two paths: build a custom MCP server (I’ll call it the DIY MCP approach) for autonomous testing or use a platform that’s already built. Let’s see what each path involves:

Playwright MCP server

Building a Playwright MCP server is time-consuming and not suitable for everyone. You’ll have to write prompts for every test scenario, build retry logic for when AI fails, and manage API tokens for MCP (otherwise, your costs will skyrocket). To see the results, you’ll need to build custom dashboards, integrate APIs, add CI/CD, and much more.

The upfront investment of your time is huge (anywhere from 2 weeks to months), and ongoing maintenance is expected to take 5-10 hours on a weekly basis. Keep in mind that you’ll also need a senior engineer along with DevOps support.

Managed AI QA (QA.tech)

If you decide to go down the managed AI route, you’re going to save up on your time, cost, and logistics. The AI agent is pre-trained on how web apps work. The whole setup basically amounts to one-click account creation: no server configuration, no browser management, none of the extra overhead. You’ll get proper session management, error handling, context retention, and built-in CI/CD, alongside automatic updates that won’t break your tests.

With this approach, you’ll spend 1-2 hours on initial setup and roughly 1 hour a week on maintenance. You don’t need a special team member, as it can all be done by any developer who can write a test description.

In the table below, you’ll find a quick overview of how the two approaches compare:

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How QA.tech Solves the DIY Approach

The QA.tech platform is built around the idea that developers want the results of AI testing, not the maintenance burden that often comes with it.

Here’s what you’ll get by using it:

• Autonomous discovery: You don’t need to teach the AI how to navigate your application. Instead, the agent will scan the app and create a semantic map of each user's interaction flow, generating a visual representation of it. For example, it can identify the way “Login” behaves regardless of the name of the button, whether it’s “btn-login” or “submit-auth”.
• Self-healing tests: With QA.tech, you don’t have to rewrite prompts when UI changes (unlike with Playwright MCP, where there is a possibility for your tests to break).
• Detailed logs: When a failure occurs within the QA.tech platform, you will not just get a “False” boolean response. Instead, you will receive a video recording of what the user saw precisely, the console logs and all networking requests leading up to the error and during the time it occurred, and a button that will enable you to export the test case directly to Linear or Jira.
• Easy migration from Playwright: If you have already developed tests using Playwright or Playwright MCP, all that work is not lost. QA.tech lets you redevelop existing tests on its own platform, thereby enabling faster implementation. And the skills you’ve built while creating automated tests remain valuable regardless.

Conclusion

Playwright MCP isn’t a bad tool. It should simply be used in specific circumstances.

For example, if you’re working toward building an internal tool or a custom solution, Playwright MCP is a great choice. But if you need to test product features quickly and don’t have weeks or months to invest, a platform like QA.tech could be the smarter solution.

Ask yourself: does building custom AI testing infrastructure really move your business forward, or would you rather dedicate that engineering time to your actual product?

If you want to see this difference in action, book a demo call with us today and create your first AI-powered test in minutes.

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