Using MCP Server to Supercharge Playwright Automation

Automation at scale often struggles with two pain points:

Slow test development (boilerplate code, selectors, retries).

Flaky tests due to fragile locators.

With Model Context Protocol (MCP), we can connect Playwright to an AI server that assists in writing, fixing, and maintaining automation tests. In this blog, we’ll set up MCP with Playwright to automate real-world scenarios on Amazon.com.

🔹 1. Prerequisites

Make sure you have the following installed:

Node.js (>=18)

Playwright

MCP server implementation (e.g., mcp-server
or custom server)

OpenAI/GitHub Copilot or another LLM provider

Install Playwright

npm init playwright@latest

Add TypeScript config if needed:

npm install -D typescript ts-node @types/node

🔹 2. Folder Structure

playwright-amazon/
 ┣ tests/
 ┃ ┣ amazon-search.spec.ts
 ┃ ┣ amazon-cart.spec.ts
 ┣ mcp/
 ┃ ┣ server.ts
 ┃ ┣ config.json
 ┣ playwright.config.ts
 ┗ package.json

🔹 3. MCP Server Setup

Your MCP server is the middleware between Playwright and the LLM. It provides structured prompts like “Suggest resilient Playwright locator for Add to Cart button”.

Example MCP Config (mcp/config.json)

{
  "name": "playwright-mcp",
  "description": "MCP server for Playwright automation",
  "capabilities": {
    "selectors": true,
    "code-gen": true
  },
  "llmProvider": "openai",
  "llmModel": "gpt-4o-mini"
}

Example MCP Server (mcp/server.ts)

import express from 'express';
import bodyParser from 'body-parser';
import { generateLocator } from './utils/locator-generator';

const app = express();
app.use(bodyParser.json());

app.post('/locator', async (req, res) => {
  const { request, context } = req.body;
  const locator = await generateLocator(request, context);
  res.json({ locator });
});

app.listen(4000, () => console.log('MCP server running on port 4000'));

🔹 4. Playwright + MCP Integration

We’ll call MCP server from within our Playwright tests whenever we need AI-assisted selectors or snippets.

Utility to Call MCP (tests/utils/mcp-client.ts)

import axios from 'axios';

export async function getLocator(request: string, context: string): Promise<string> {
  const response = await axios.post('http://localhost:4000/locator', { request, context });
  return response.data.locator;
}

🔹 5. Example Test – Amazon Search

tests/amazon-search.spec.ts

import { test, expect } from '@playwright/test';
import { getLocator } from './utils/mcp-client';

test('Amazon Search for Laptop', async ({ page }) => {
  await page.goto('https://www.amazon.com');

  // Get locator dynamically from MCP
  const searchBox = await getLocator("Search input field", "amazon-homepage");
  await page.fill(searchBox, 'Laptop');

  const searchButton = await getLocator("Search button", "amazon-homepage");
  await page.click(searchButton);

  // Validate results
  const results = page.locator('span.a-size-medium');
  await expect(results.first()).toBeVisible();
});

Here MCP generates resilient locators, so even if Amazon updates their DOM slightly, the AI can adapt.

🔹 6. Example Test – Add to Cart

tests/amazon-cart.spec.ts

import { test, expect } from '@playwright/test';
import { getLocator } from './utils/mcp-client';

test('Add product to Amazon Cart', async ({ page }) => {
  await page.goto('https://www.amazon.com');

  // Search product
  const searchBox = await getLocator("Search input field", "amazon-homepage");
  await page.fill(searchBox, 'iPhone 15');
  const searchButton = await getLocator("Search button", "amazon-homepage");
  await page.click(searchButton);

  // Click first result
  const firstResult = await getLocator("First product link", "amazon-search-results");
  await page.click(firstResult);

  // Add to cart
  const addToCart = await getLocator("Add to Cart button", "amazon-product-page");
  await page.click(addToCart);

  // Validate cart
  const cartCount = page.locator('#nav-cart-count');
  await expect(cartCount).toHaveText('1');
});

🔹 7. Benefits

✅ Resilient selectors – MCP auto-generates robust locators.
✅ Reduced flaky tests – Fewer selector breakages on UI changes.
✅ Faster automation – Engineers describe tests; AI scaffolds code.
✅ Scalable – Can plug into CI/CD for continuous improvement.

🔹 Conclusion

Using MCP with Playwright transforms how we build automation. Instead of spending hours debugging fragile selectors, engineers can focus on test strategy, while MCP + AI handles the scaffolding.