Manual Testing with Playwright MCP – Have you ever felt that a simple manual test should be less manual?
For years, quality assurance relied on pure human effort to explore, click, and record. But what if you could perform structured manual and exploratory testing, generate detailed reports, and even create test cases—all inside your Integrated Development Environment (IDE), using zero code?
I’ll tell you this: there’s a tool that can help us perform manual testing in a much more structured and easy way inside the IDE: Playwright MCP.
Section 1: End the Manual Grind – Welcome to AI-Augmented QA
The core idea is to pair a powerful AI assistant (like GitHub Copilot) with a tool that can control a real browser (Playwright MCP). This simple setup is done in only a few minutes.
The Essential Setup for Manual Testing with Playwright MCP: Detailed Steps
- For this setup, you will integrate Playwright MCP as a tool that your AI agent can call directly from VS Code.
1. Prerequisites (The Basics)
- VS Code installed in your system.
- Node.js (LTS version recommended) installed on your machine.
2. Installing GitHub Copilot (The AI Client)
- Open Extensions: In VS Code, navigate to the Extensions view (Ctrl+Shift+X or Cmd+Shift+X).
- Search and Install: Search for “GitHub Copilot” and “GitHub Copilot Chat” and install both extensions.
- Authentication: Follow the prompts to sign in with your GitHub account and activate your Copilot subscription.
- GitHub Copilot is an AI-powered code assistant that acts almost like an AI pair programmer.
After successful installation and Authentication, you see something like below
3. Installing the Playwright MCP Server (The Browser Tool)
Playwright MCP (Model Context Protocol): This is the bridge that provides browser automation capabilities, enabling the AI to interact with the web page.
- The most direct way to install the server and configure the agent is via the official GitHub page:
- Navigate to the Source: Open your browser and search for the Playwright MCP Server official GitHub page (https://github.com/microsoft/playwright-mcp).
- The One-Click Install: On the GitHub page, look for the Install Server VSCode button.
- Launch VS Code: Clicking this button will prompt you to open Visual Studio Code.
- Final Step: Inside VS Code, select the “Install server” option from the prompt to automatically add the MCP entry to your settings.
- To verify successful installation and configuration, follow these steps:
- Click on “Configure Tool” icon
- After clicking on the “configure tool “ icon, you see the tools of Playwright MCP as shown in the below image.
- After clicking on the “Settings” icon, you see the “Configuration (JSON)” file of Playwright MCP, where you start, stop, and restart the server as shown in image below
{
"servers": {
"playwright": {
"command": "npx",
"args": [
"@playwright/mcp@latest"
],
"type": "stdio"
}
},
"inputs": []
} 1. Start Playwright MCP Server:
After the Playwright MCP Server is successfully configured and installed, you will see the output as shown below.
2. Stop and Restart Server
This complete setup allows the Playwright MCP Server to act as the bridge, providing browser automation capabilities and enabling the GitHub Copilot Agent to interact with the web page using natural language.
Section 2: Phase 1: Intelligent Exploration and Reporting
The first, most crucial step is to let the AI agent, powered by the Playwright MCP, perform the exploratory testing and generate the foundational report. This immediately reduces the tester’s documentation effort.
Instead of manually performing steps, you simply give the AI Agent your test objective in natural language.
The Exploration Workflow:
- Exploration Execution: The AI uses discrete Playwright MCP tools (like browser_navigate, browser_fill, and browser_click) to perform each action in a real browser session.
- Report Generation: Immediately following execution, the AI generates an Exploratory Testing Report. This report is generated on the basis of the exploration, summarizing the detailed steps taken, observations, and any issues found.
Our focus is simple: Using Playwright MCP, we reduce the repetitive tasks of a Manual Tester by automating the recording and execution of manual steps.
Execution Showcase: Exploration to Report
Input (The Prompt File for Exploration)
This prompt directs the AI to execute the manual steps and generate the initial report.
Prompt for Exploratory Testing
Exploratory Testing: (Use Playwright MCP)
Navigate to https://www.demoblaze.com/. Use Playwright MCP Compulsory for Exploring the Module <Module Name> and generate the Exploratory Testing Report in a .md file in the Manual Testing/Documentation Directory.Output (The Generated Exploration Report)
The AI generates a structured report summarizing the execution.
Live Browser Snapshot from Playwright MCP Execution
Section 3: Phase 2: Design, Plan, Execution, Defect Tracking
Once the initial Exploration Report is generated, QA teams move to design specific, reusable assets based on these findings.
1. Test Case Design (on basis of Exploration Report)
The Exploration Report provides the evidence needed to design formal Test Cases. The report’s observations are used to create the Expected Results column in your CSV or Test Management Tool.
- The focus is now on designing reusable test cases, which can be stored in a CSV format.
- These manually designed test cases form the core of your execution plan.
- We need to provide the Exploratory Report for References at the time of design test Cases.
- Drag and drop the Exploratory Report File as context as shown in the image below.
Input (Targeted Execution Prompt)
This prompt instructs the AI to perform a single, critical verification action from your Test Case.
Role: Act as a QA Engineer.
Based on Exploratory report Generate the Test cases in below of Format of Test Case Design Template
=======================================
🧪 TEST CASE DESIGN TEMPLATE For CSV File
=======================================
Test Case ID – Unique identifier for the test case (e.g., TC_001)
Test Case Title / Name – Short descriptive name of what is being tested
Preconditions / Setup – Any conditions that must be met before test execution
Test Data – Input values or data required for the test
Test Steps – Detailed step-by-step instructions on how to perform the test
Expected Result – What should happen after executing the steps
Actual Result – What happened (filled after execution)
Status – Pass / Fail / Blocked (result of the execution)
Priority – Importance of the test case (High / Medium / Low)
Severity – Impact level if the test fails (Critical / Major / Minor)
Test Type – (Optional) e.g., Functional, UI, Negative, Regression, etc.
Execution Date – (Optional) When the test was executed
Executed By – (Optional) Name of the tester
Remarks / Comments – Any additional information, observations, or bugs found Output (The Generated Test cases)
The AI generates structured test cases.
2. Test Plan Creation
- The created test cases are organized into a formal Test Plan document, detailing the scope, environment, and execution schedule.
Input (Targeted Execution Prompt)
This prompt instructs the AI to perform a single, critical verification action from your Test Case. 2
Role: Act as a QA Engineer.
- Use clear, professional language.
- Include examples where relevant.
- Keep the structure organized for documentation.
- Format can be plain text or Markdown.
- Assume the project is a web application with multiple modules.
generate Test Cases in Form Of <Module Name >.txt in Manual Testing/Documentation Directory
Instructions for AI:
- Generate a complete Test Plan for a software project For Our Test Cases
- Include the following sections:
1. Test Plan ID
2. Project Name
3. Module/Feature Overview
4. Test Plan Description
5. Test Strategy (Manual, Automation, Tools)
6. Test Objectives
7. Test Deliverables
8. Testing Schedule / Milestones
9. Test Environment
10. Roles & Responsibilities
11. Risk & Mitigation
12. Entry and Exit Criteria
13. Test Case Design Approach
14. Metrics / Reporting
15. Approvals Output (The Generated Test plan)
The AI generates structured test plan of designed test cases.
3. Test Cases Execution
This is where the Playwright MCP delivers the most power: executing the formal test cases designed in the previous step.
- Instead of manually clicking through the steps defined in the Test Plan, the tester uses the AI agent to execute the written test case (e.g., loaded from the CSV) in the browser.
- The Playwright MCP ensures the execution of those test cases is fast, documented, and accurate.
- Any failures lead to immediate artifact generation (e.g., defect reports).
Input (Targeted Execution Prompt)
This prompt instructs the AI to perform a single, critical verification action from your Test Case.
Use Playwright MCP to Navigate “https://www.demoblaze.com/” and Execute Test Cases attached in context and Generate Test Execution Report.First, Drag and drop the test case file for references as shown in the image below.
Live Browser Snapshot from Playwright MCP Execution
Output (The Generated Test Execution report)
The AI generates structured test execution report of designed test cases.
4. Defect Reporting and Tracking
If a Test Case execution fails, the tester immediately leverages the AI Agent and Playwright MCP to generate a detailed defect report, which is a key task in manual testing.
Execution Showcase: Formal Test Case Run (with Defect Reporting)
We will now execute a Test Case step, intentionally simulating a failure to demonstrate the automated defect reporting capability.
Input (Targeted Execution Prompt for Failure)
This prompt asks the AI to execute a check and explicitly requests a defect report and a screenshot if the assertion fails.
Refer to the test cases provided in the Context and Use Playwright MCP to execute the test, and if there is any defect, then generate a detailed defect Report. Additionally, I would like a screenshot of the defect for evidence.
Output (The Generated Defect report and Screenshots as Evidence)
The AI generates a structured defect report of designed test cases.
Conclusion: Your Role is Evolving, Not Ending
Manual Testing with Playwright MCP is not about replacing the manual tester; it’s about augmenting their capabilities. It enables a smooth, documented, and low-code way to perform high-quality exploratory testing with automated execution.
- Focus on Logic: Spend less time on repetitive clicks and more time on complex scenario design.
- Execute Instantly: Use natural language prompts to execute tests in the browser.
- Generate Instant Reports: Create structured exploratory test reports from your execution sessions.
- Future-Proof Your Skills: Learn to transition seamlessly to an AI-augmented testing workflow.
It’s time to move beyond the traditional—set up your Playwright MCP today and start testing with the power of an AI-pair tester!
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Automation Testing | Playwright MCP | QA & AI Integration