In this Blog, We will discuss about "Locator Labs" on finding reliable and efficient locators to build your test automation. First of all thanks to "Naveen Khunteta" in building this amazing chrome extension.

To start with,

1. What is Locator Labs?

Locator Labs is a Chrome extension that lets QA engineers and automation developers:

Inspect web elements visually
Generate multiple locator strategies (getByRole, getByLabel, CSS, XPath)
Validate locators on the page instantly
Export working locators into Playwright or Selenium Page Object code

Supports:
✔ Playwright (JavaScript / TypeScript / Python)
✔ Selenium (Java / Python / C#)

📘 Diagram: “Where Locator Labs Fits in the Automation Flow”

2. Installation Guide

Follow these steps to install Locator Labs in your Chrome or Edge browser:

Open the Chrome Web Store page
Click Add to Chrome
Approve the permission prompt
Once installed, pin the extension to your toolbar for quick access
Navigate to any web page and click the Locator Labs icon

🧭 Diagram: “Installation Steps Overview”

3. Using Locator Labs to Generate Locators

Once installed, it’s incredibly easy to start finding locators.

Step 1 — Open your target web page

Go to the page you want to automate (for example, your React app).

Step 2 — Activate Locator Labs

Click the Locator Labs icon in the Chrome toolbar. A side panel or overlay appears.

Step 3 — Hover and Select

Hover your mouse over any element. Locator Labs highlights it and instantly shows suggested locators like:

Step 4 — Copy or Export

Copy the locator or export it into a Page Object in your desired framework.

🧮 Diagram: “How Locator Labs Generates Locators”

4. Validating Locators in Real Time

Locator Labs also lets you validate your locators instantly.

✅ Click on a suggested locator → the element is highlighted on the page
❌ If multiple matches appear, it shows the count and highlights all of them

You can refine your locator using filters or choose alternate strategies.

5. Exporting to Your Automation Framework

After validation, export the locator into ready-to-use automation code.

Playwright Export Example

Selenium Export Example

6. Best Practices with Locator Labs

✅ Prefer semantic locators (getByTestId, getByRole, getByLabel) for stability
🧩 Re-validate locators after UI changes
🧠 Don’t rely only on long XPaths
🔁 Store all locators in Page Object Models
🧾 Use built-in annotation/screenshot for reporting

🪄 Diagram: “Locator Labs in a QA Workflow”

7. Conclusion

Locator Labs is one of the most practical Chrome extensions for test automation engineers.
It simplifies finding, validating, and maintaining locators across Playwright and Selenium projects.

It is a helpful productivity-tool for test automation teams using Playwright or Selenium. It bridges the gap between manual locator hunting and robust, maintainable automation code. By generating smart suggestions, validating them in real-time, exporting ready-to-use Page Objects, and supporting relative locators (for Selenium) as well as typical locators (for Playwright), it can save a lot of time and reduce flakiness.

If you’ve ever struggled with flaky selectors, this tool can drastically reduce that pain.
Try it on your next automation sprint — it might just become your go-to locator companion.

If you’re regularly writing UI tests for your web application, incorporating a tool like Locator Labs can make your locator creation workflow nicer and more reliable.

Happy Automation testing Guys :)!

In this Blog, We will look into the difference of using Playwright MCP over other LLMs and Agents like Bolt, Perplexity.ai, Warp.dev, V0.app, Manus.im, and Qwen.ai

🔍 What is Playwright MCP (Model Context Protocol)

Playwright MCP is an open-source server developed by Microsoft that enables Large Language Models (LLMs) such as GitHub Copilot, Claude, or GPT to interact with web pages using Playwright's browser automation tools. By leveraging the Model Context Protocol (MCP), it allows AI models to perform tasks like reading web content, interacting with elements, taking screenshots, and executing JavaScript within a browser environment.

It allows AI tools to:

Access your local Playwright test project (code, tests, config files)
Run commands or scripts on your local environment (e.g., execute npx playwright test)
Inspect page structure, selectors, or test results dynamically
→ Essentially, your AI model can “see” and “interact” with your actual test environment — not just generate code in isolation.

⚖️ Comparison: Playwright MCP vs Other Tools

Tool	Core Nature	Integration Level	Context Awareness	Automation Test Generation Quality	Unique Advantage
Playwright MCP	AI-connected runtime (direct access to local Playwright)	Deep (native with your project)	🔥 High (accesses live files & browser context)	Very High	Contextual test creation, can validate selectors, debug live
Playwright Codegen	Built-in recorder (CLI/UI)	High (native)	Low (records user actions only)	Medium	Great for quick script generation; not AI-based
Bolt.new	AI test generation from plain text	Medium	Medium	Medium-High	Fast UI → test translation for Playwright or Cypress
Perplexity.ai	AI research + coding assistant	Low	Medium	Medium	Good for explaining or troubleshooting Playwright concepts
Warp.dev	AI terminal with command suggestions	Low	Low	Low	Helpful for CLI automation setup, not for writing test logic
V0.app	AI UI generation + code	Medium	Medium	Medium	Great for scaffolding test dashboards or UIs, not test logic
Manus.im	AI code assistant	Medium	Medium	Medium	Useful for maintaining test frameworks; lacks runtime access
Qwen.ai	AI model (strong coding focus)	Low	Medium	Medium	Efficient at generating scripts, but lacks local integration

🧩 Key Takeaway:

👉 Playwright MCP is the only tool that actually executes, inspects, and validates tests within your local Playwright environment. All others are text/code generators or assistants — not real-time test agents.

👉 Playwright MCP is the only one that connects an AI directly to your running Playwright test environment, not just to text/code prompts.

⚙️ 1. Core Difference — What They Are

Tool	What It Actually Is	What It Does
Playwright MCP	AI-connected runtime (via Model Context Protocol)	Lets AI interact directly with your local Playwright environment — access files, run tests, inspect DOMs, debug live.
Playwright Codegen	Built-in recorder tool from Playwright	Records browser actions and auto-generates Playwright scripts.
Bolt.new	AI test generator	Converts plain English test cases → Playwright/Cypress code using AI.
Perplexity.ai	AI research + coding assistant	Answers questions, explains concepts, and sometimes writes code — but not integrated into your test environment.
Warp.dev	AI-powered terminal	Helps run commands, set up Playwright, or fix CLI errors — but doesn’t generate or run tests.
V0.app	AI UI-to-code generator (from Vercel)	Builds UI components or test dashboards — not specific to test automation.
Manus.im	AI pair-programmer	Helps write or refactor test code — works like GitHub Copilot.
Qwen.ai	General-purpose AI code model	Good at generating Playwright scripts, but lacks deep context about your local project.

🧠 Context Awareness

Tool	Context Used	Context Depth
Playwright MCP	Real project files, browser context, selectors, and runtime	🔥 High (live runtime + codebase)
Playwright Codegen	User interactions during recording	Medium
Bolt.new	Prompt text only	Low
Perplexity.ai	Web + general knowledge	Low
Warp.dev	CLI context	Low
V0.app	UI design context	Low
Manus.im	Local code context (IDE plugin)	Medium
Qwen.ai	Prompt text + sample code	Medium

🧩 Summary:
Only Playwright MCP has true runtime context — it can read your test files, inspect the app under test, and adjust code based on real DOM changes.

🧪 Test Generation & Execution Capability

Tool	Can Generate Tests?	Can Execute or Validate Tests?
Playwright MCP	✅ Yes	✅ Yes — runs tests locally & returns results
Playwright Codegen	✅ Yes (by recording).	✅ Yes (via Playwright CLI)
Bolt.new	✅ Yes	❌ No
Perplexity.ai	✅ Limited	❌ No
Warp.dev	❌ No	✅ Yes (if you type commands manually)
V0.app	❌ Not its goal	❌ No
Manus.im	✅ Yes	❌ No
Qwen.ai	✅ Yes	❌ No

🧩 Summary:
Only Playwright MCP and Playwright Codegen can run tests directly — but MCP adds AI intelligence and project awareness to that process.

💬 Type of AI Involvement

Tool	AI Role	Intelligence Type
Playwright MCP	Active collaborator	Contextual + operational (reads files, executes commands, reasons with results)
Playwright Codegen	Passive recorder	Rule-based, no AI
Bolt.new	Prompt-base generator	Generative AI (LLM)
Perplexity.ai	Knowledge assistant	Conversational AI
Warp.dev	CLI enhancer	Command recommendation AI
V0.app	UI generator	Design-to-code AI
Manus.im	Code assistant	Pair-programmer AI
Qwen.ai	Coding LLM	Code completion / generation AI

🧩 Summary:
Playwright MCP blends LLM intelligence + local execution, which none of the others do.

🧑‍🔧 Benefits for Different Users

Role	Benefit of Playwright MCP
Manual Testers	Can describe scenarios in plain English, auto-generate and execute real tests, and learn Playwright syntax interactively.
Automation Engineers	AI can debug, refactor, and maintain tests with full context of your project and selectors.
Developers	Quick test scaffolding, auto-fix flaky tests, live feedback without switching tools.

In contrast, other tools only cover a slice of that workflow — e.g., generating code (Bolt.new), recording steps (Codegen), or answering questions (Perplexity.ai).

🚀 Why Playwright MCP Stands Out

Feature	Playwright MCP Advantage
Real project interaction	AI can open your files, run tests, and see results.
Selector validation	Can test locators against the live DOM and fix them automatically.
AI-assisted debugging	Explains failures and suggests fixes right from the console.
One-step test creation	No switching between tools or copy-paste.
Hybrid for all skill levels	Low-code for testers, full-code for engineers.

💡 Example: Live Workflow Using Playwright MCP

Prompt (in your AI tool):

“Generate a Playwright test that logs into the demo site, verifies the dashboard title, and captures a screenshot if login fails.”

What MCP does:

Reads your local project structure (tests/login.spec.ts, playwright.config.ts)
Opens the browser context via Playwright
Validates selectors live (page.locator('#login-btn'))
Generates the complete runnable test file
Executes the test and returns the report/screenshot in your chat

No other AI tool (Perplexity, Warp, etc.) can currently run and validate tests inside your actual environment.

🏁 In Simple Terms

Playwright MCP = “AI that actually lives inside your Playwright project.”
While others (Bolt.new, Qwen.ai, Manus.im, etc.) are AI that just talk about your Playwright project.

🧩 “Playwright MCP = Context + Execution + AI Reasoning”

Vs

⚙️ “Others = Code Generation or Explanation Only.”

🔮 Conclusion

While Playwright MCP introduces an exciting concept — connecting AI directly with your local Playwright environment — it’s still in an early, experimental stage. Its setup complexity, limited stability, and dependency on specific AI integrations mean it’s not yet the best choice for most teams.

If your goal is fast, reliable test generation or assistance, tools like Bolt.new, Manus.im, or Qwen.ai are currently more practical and mature.

Use Bolt.new for quick English-to-test conversions.
Use Perplexity.ai for research, troubleshooting, and clarifying Playwright concepts.
Use Manus.im or Qwen.ai for code completion and framework scaffolding.
Use Playwright Codegen for dependable, record-and-playback script creation.

Each of these tools excels in its specific domain — whether that’s learning, generating, or maintaining tests — and can fit naturally into existing QA workflows without the friction MCP still carries.

So for now, Playwright MCP is an interesting glimpse into the future, but the smarter choice today is to pick the right AI tool for the right purpose — rather than relying on MCP to do it all.

Happy automation testing guys 🚀🚀🚀!

Frontend Test Automation

Thursday, October 30, 2025

Locator Labs — A Smart Way to Find Locators for Playwright & Selenium