In what ways can testers apply Model Context Protocol custom tools to streamline Playwright-based Electron application testing?
Can MCP be used for generating and maintaining BDD-style test scenarios?
As testing sidekicks grow in complexity (e.g., adding AI-powered analysis, supporting new browsers), what strategies ensure MCP-based tools remain maintainable? How do you balance innovation (e.g., experimenting with new MCP features) with stability
Can MCP be used in shift-left testing to guide developers on writing better unit tests?
How do you balance human oversight with agent automation to protect safety/ethics without slowing CI/CD?
What evidence shows agent-to-agent testing surfaces latent multi-step failure modes, and how are these verified?
How do you collect feedback from your AI tool’s usage to evolve and improve its recommendations over time?
What is the biggest advantage of Playwright compared to Webdriveio?
How does MCP integrate with existing CI/CD pipelines without adding significant overhead or latency?
Why cant we built an AI Agent to do these things rather than using or building an MCP ?
Can we use MCP foe all functional testing? I see it lacks cross browser test support. What approach we should go for while using MCP?
Attending the session made me see that MCP shines for repetitive or boilerplate tests, but for edge cases and domain-specific flows, I often prefer writing tests myself. Over-refactoring MCP scripts can actually slow things down if the test logic hasn’t changed.
MCP-based assistants can slot into CI/CD pipelines pretty smoothly if you treat them like asynchronous helpers. They can run in parallel with the rest of your pipeline, so nothing gets held up. The main thing is to keep an eye on their response times, making sure they don’t turn into a bottleneck. Done right, they can actually make your testing flow faster and more efficient.
AI can do a pretty good job of predicting user behavior and spotting things that don’t work as expected, but it doesn’t actually experience the product like a human does. When it comes to the subtle touches, the little frustrations or delights that make UX feel smooth human intuition is still key. That’s something we really dug into during the session.
Ah, I remember Viktoria’s session on this, it was super insightful! So, regarding MCP tools, the key thing to remember is that they do interact with testing frameworks and, yes, sometimes sensitive data. That’s why it’s really important to be careful about how we let them run.
From what I gathered, a practical approach is twofold. First, sandbox your experiments—basically, give the tool its own safe little playground where it can do its thing without risking your main environment or exposing sensitive data. Second, make sure any data the tool touches is sanitized, so nothing sensitive leaks out.
On the team side, role-based access is huge. Not everyone should have free rein—letting only the right people run certain experiments keeps things secure without killing innovation. So, in practice, I’ve seen teams create isolated experiments where they can play, test, and iterate, but in a controlled way that keeps security intact. It’s really about finding that sweet spot between trying new ideas and keeping your data safe.
You know, one thing that really clicked for me during the session was that you don’t need to be a machine learning wizard to get real value out of MCP. The way I see it, if you give MCP a clear idea of your product context like the key user flows, features, and workflows, it can basically act as your testing sidekick.
It starts suggesting useful, meaningful test cases tailored to your product, without you having to dive into complex modeling or heavy AI setups. I found it super practical because it makes AI approachable for everyday testers, it’s like having a helpful teammate that just “gets” your app.
Oh, this part about the testing sidekick using the Model Context Protocol (MCP) was super interesting! Basically, the sidekick doesn’t just follow a rigid set of instructions, it actually “understands” what’s going on in the app at any given moment. For example, it can tell which modules are active, what part of the user flow you’re in, who the user persona is, or even what environment the app is running in.
What I found really cool is that it can reason over multiple steps, which makes it perfect for apps that don’t behave in a totally predictable way. So instead of blindly running tests, it adapts on the fly, kind of like having a teammate who’s always aware of the context and can adjust their strategy in real-time. It makes testing way smarter and more flexible.
Ah yes! I caught this session at Testmu 2025 on this, it was really insightful. So, if you’re looking to test an Electron app with Playwright using Model Context Protocol (MCP) custom tools, here’s how I’d put it in practical terms:
Think of MCP as a way to make your tests smarter and more adaptive. Instead of writing rigid scripts, you can let the tool understand the current state or “context” of your app and generate tests dynamically. For an Electron app, that means your tests can react to different windows, dialogs, or components as they appear, without you having to manually account for every scenario upfront.
A good approach is to start small: focus on the core modules of your app first. Get those tests solid and reliable. Once you have that foundation, you can gradually expand your coverage to other features. This way, you’re not overwhelming yourself with too many moving parts at once, and your tests remain maintainable.
In short, it’s all about letting MCP do the heavy lifting of context-aware test generation, while you guide it step by step. Makes testing Electron apps feel way less like juggling and more like having a helpful sidekick.
MCP is actually pretty handy when it comes to testing SDKs or simulating UAT flows. It can handle multi-step validations and really gives you a sense of how your application behaves in real-world scenarios. That said, for deep code-level analysis or really intricate configurations, a human still needs to step in.
In the session, there was this example where MCP picked up latent failures that standard static tests completely missed. So, it’s not about replacing human testing, it’s more like having a super-smart sidekick that catches things you might overlook and speeds up your testing process.
When it comes to making the testing sidekick handle errors gracefully, like if MCP servers go down or send back unexpected data the approach is pretty practical. Viktoria highlighted using retries, fallback strategies, and monitoring server health.
From my own experience trying this out, these strategies really work. If the server hiccups or returns something unusual, the testing flow doesn’t just crash, it either retries the request or uses a fallback, keeping everything running smoothly. It’s a simple mindset shift, but it makes the sidekick much more reliable in real-world scenarios.