Hello Everyone,
Yes, Absolutely! Playwright MCP can actually spot when a locator stops working and will suggest alternatives on the fly. This means you don’t have to spend so much time manually fixing broken locators—making your test automation much smoother and less of a headache.
Think of it like this: when you run your tests across multiple browsers at the same time with Playwright MCP, AI-powered debugging keeps an eye on how each element behaves in each browser. If something only goes wrong in, say, Chrome but works perfectly in Firefox and Edge, the AI can flag that as a browser-specific quirk rather than a real bug in your app. It basically helps you separate ‘browser weirdness’ from actual application issues, saving a ton of time in figuring out what’s really broken
When I create prompts for AI testing, I try to be super clear about what I want, the goal, the expected behavior, and any constraints. Once I get the AI’s output, I look at how relevant it is, how well it covers what I asked, and the probability of it hitting the mark. Based on that, I tweak my prompts for the next round. It’s a bit like teaching the AI step by step to understand exactly what you need
Think of it like this: instructions are the “rules of the game” you give the AI, they tell it how to behave or what constraints to follow, like “always explain things step by step” or “keep the tone friendly.” On the other hand, prompts are more like the actual task or question you want the AI to tackle, such as “write a summary of this article” or “generate a test case for this scenario.”
When you combine both, clear instructions plus a well-crafted prompt, you get much better results because the AI knows not just what to do, but also how to do it.
Yeah, so what I usually do is take the user stories and let AI help parse them, basically, it highlights the key fields and actions we need to automate. From there, it can suggest the right selectors for those elements. Tools like Playwright MCP are great because they can even pre-fill locators for you, which saves a ton of time. That said, I always double-check them myself, AI is super helpful, but having that human validation ensures everything actually works as expected in the real app.
Honestly, from what I’ve seen, a hybrid approach tends to work really well. I’d keep a library of your core UI flows that you know are stable and important, basically the stuff you always want to cover. Then, for new features or less predictable paths, you can lean on AI to dynamically generate tests with Playwright MCP. This way, you get the best of both worlds: consistency for your essential tests and flexibility for exploring new scenarios without manually writing everything.
Yes, that can happen sometimes. There are occasions where MCP might show a test as passing even if there are small UI differences that technically shouldn’t be ignored. That’s why I always make it a habit to double-check the critical flows manually, just to be safe.
Playwright MCP uses AI to make debugging a lot less painful. Basically, it keeps an eye on your tests over time, notices patterns where things usually fail, and can even predict potential trouble spots before they break. On top of that, it can suggest better ways to locate elements on the page and even automatically tweak or rewrite parts of your test that are prone to breaking. So, instead of chasing flaky tests manually, the AI gives you practical fixes and helps your automation stay more stable.
When it comes to debugging race conditions in parallel tests, one approach we’ve found really helpful is leveraging AI. Basically, it can spot timing conflicts and highlight when different tests are accidentally stepping on each other’s toes, like sharing the same state at the same time. From there, the AI can suggest ways to handle it, like synchronizing certain steps or staggering test execution, which helps prevent those annoying flaky failures.
Ah, yes! When you run out of your monthly Claude tokens, the easiest way we’ve handled it is by reaching out to our platform admin, they can either upgrade the plan or allocate extra tokens to your project. On top of that, a little trick that really helps stretch your usage is caching responses wherever possible. That way, you don’t have to keep hitting the API for the same requests and can make your tokens go further.
When you’re running tests across multiple browsers with Playwright MCP, things can get tricky because each browser might behave a little differently. This is where AI-powered debugging really shines. It can automatically look at things like DOM changes, events, and how elements are rendering in each browser. By doing this, it helps pinpoint whether a problem is just a browser quirk or an actual bug in your application. So instead of guessing or manually checking each browser, AI gives you clear insights fast.
Right now, these agent modes like the generator are mainly built with VS Code in mind, so that’s where you’ll get the smoothest experience. That said, some VS Code forks can still work with them, as long as the necessary extensions are compatible. Just keep in mind that a few features might only work fully in the official VS Code environment.
From what Debbie explained, MCP is pretty smart when it comes to handling UI changes that aren’t consistent, like dynamic content or layouts that change randomly. Instead of relying on rigid locators, it uses things like dynamic locators, visual hints, and context-aware element recognition. This way, even if the layout shifts or elements appear differently, MCP can still find what it needs and keep your tests running smoothly.
Beyond just creating tests and debugging, Playwright MCP really steps up with some advanced AI-driven features. For example, it can predict potential risks in your tests before they become problems. It also auto-optimizes your test suites, so you’re not wasting time running redundant or low-impact tests. Another cool feature is dynamic prioritization, which focuses on the scenarios that matter most and are likely to catch critical issues first. And on top of that, it seamlessly integrates with CI/CD pipelines, helping teams continuously improve their automation without extra manual effort.
Absolutely! From my experience, it works best to give the AI as much structured context as you can. Sure, AI can whip up basic navigation on its own, but if you provide details like field rules, validations, or even your Figma designs, it’ll make the generated assertions much more accurate. Basically, the AI isn’t just guessing, it’s building tests that align with what you actually want the app to do, not just what it thinks it sees.
Yes! Playwright can definitely be used to test Electron-based desktop apps, and the MCP handles scripts in much the same way it does for web workflows. That said, because desktop apps can have some unique behaviors, you might need to do a bit of extra setup to handle app-specific contexts. But overall, the process is pretty similar to what you’d do for web automation.
Honestly, I usually just tell CoPilot exactly what I want by saying something like, “Show me the first solution only.” That way, it doesn’t try to give multiple “better” alternatives right away. Another thing I do is take a quick look at the first suggestion before asking it to refine or improve anything. Keeping your instructions clear really helps avoid those extra rounds and saves time.
Absolutely! MCP is actually built to handle those tricky dynamic elements. Even if your locators keep changing, it uses self-healing locators along with visual cues and context-aware selection. Basically, it can “figure out” the element you’re trying to interact with, which means less time spent fixing broken tests and more time actually testing.
Hey Everyone Hope you are doing Great!
Honestly, the best way to make the most of AI-driven debugging with Playwright is to treat it as a continuous learning loop. For example, you can hook the AI insights directly into your CI/CD pipeline. When a test flakiness or failure pops up, don’t just fix it manually, look at what the AI is suggesting. Update your scripts based on those recommendations and keep an eye on the logs regularly. Over time, this not only helps your tests become more stable but also lets the AI “learn” from your updates, improving both test coverage and the quality of the feedback you get. It’s like having a smart co-pilot that helps your automation get sharper with every run.
Yeah! From my experience, JetBrains IDEs do have support for AI-assisted coding via plugins, so you can give it a try there. That said, Playwright MCP feels like it’s more naturally built for VS Code, with smoother integration and features. So if you’re working in JetBrains, it’ll still work, but you might notice a few limitations, especially when using different agent modes. Basically, it’s doable, just not as seamless as in VS Code.