How I used Cursor, Playwright MCP, and Playwright CLI to build real automation for SwiftCart
AI will not magically turn weak tests into strong automation. But it can help QA engineers move faster when it is used the right way.
Recently, I built a small automation project for a SwiftCart e-commerce app using Playwright, TypeScript, Cursor, Playwright MCP, Context7 MCP, and GitHub Actions CI.
The biggest lesson was simple:
Playwright MCP does not replace Playwright. It helps you inspect the app faster so you can create better Playwright tests.
That difference matters.
What is Playwright
Playwright MCP lets Cursor interact with the browser through tools like:
browser_navigate
browser_click
browser_type
browser_snapshot
browser_wait_for
Regular Playwright runs your test files:
npx playwright test
Playwright MCP helps during development. It lets Cursor open the app, inspect flows, understand locators, and help generate test code.
Simple way to remember it:
Playwright MCP = helps inspect and build tests
Playwright CLI = runs the final tests
The website itself is not using MCP. Cursor is using MCP to inspect the website.
Why QA engineers should care
A big part of automation is not just writing code. It is deciding:
- What flow should be tested?
- Which locator is stable?
- What should be asserted?
- Is this a real bug or a weak test?
- Can this test run in CI?
MCP helps with the discovery part. It can inspect the app and help generate a first version faster.
But the QA engineer still has to review, clean, refactor, and validate the test.
AI can speed up the work. It should not replace judgment.
What I automated in SwiftCart
I tested three common e-commerce flows:
- Search flow
Homepage → Search input → Results page → Product results visible
- Add to cart flow
Products page → Product detail → Add to cart → Cart page → Item visible
- Checkout and login validation
Cart → Checkout → Login required → Checkout page → Form validation
These are realistic flows that QA teams often automate in e-commerce applications.
My Workflow
The workflow was:
1. Use Playwright MCP to inspect the app
2. Discover user flows and locators
3. Generate initial Playwright tests
4. Run tests with Playwright CLI
5. Fix failures
6. Refactor into Page Object Model
7. Add test.step() for better reporting
8. Add GitHub Actions CI
9. Extend into API testing
The key point: I did not treat AI output as final code. I used it as a starting point, then cleaned it into a maintainable automation framework.
Api Testing Extension
After the UI tests, I also created an API testing repo using Playwright request API.
UI test:
Open browser → click buttons → check page behavior
API test:
Send request directly to backend → check status code and JSON response
For SwiftCart, I tested endpoints like:
GET /products
GET /products/{id}
The API tests checked:
- status code is 200
- response is JSON
- products exist
- product fields exist
- invalid product IDs return 404
API tests are much faster because they do not open a browser.
