API Testing with LLM(Claude) and Playwright MCP (Model Context Protocol)

In today’s fast-evolving software landscape, delivering high-quality applications at scale is a constant challenge. Traditional API testing often involves writing complex scripts, maintaining boilerplate code, and dealing with brittle test suites. Enter Playwright MCP (Model Context Protocol) combined with Large Language Models (LLMs) — a revolutionary duo that simplifies API automation by enabling testers to write tests in plain English, reducing coding overhead, and enhancing test efficiency.

In this blog, we’ll explore how Playwright MCP, powered by LLMs like Claude, transforms API automation, with practical examples drawn from the ExecuteAutomation Playwright MCP repository.

👌You’ll be amazed at how smart the Playwright MCP server is.
For example, when making a PUT request, it doesn’t just update the data — it also shows you the before and after values, giving you full visibility into what changed. A great feature for debugging and validation! 💡.

Grab Your Copy of the Book “Web Automation Testing Using Playwright” Here:👇

Web Automation Testing Using Playwright: End-to-end, API, accessibility, and visual testing using…

Why Modern API Automation Needs a New Approach

API testing is critical for ensuring seamless communication between application components. However, traditional tools like Postman, RestAssured, or even Playwright’s native API testing capabilities come with challenges:

• Code-Heavy Scripts: Writing and maintaining test scripts requires significant coding expertise.
• Dynamic APIs: Modern APIs with complex payloads or authentication mechanisms (e.g., OAuth) are hard to test consistently.
• Flaky Tests: Environmental issues or timing problems often lead to unreliable test results.
• Accessibility: Non-technical team members, like QA analysts, struggle to contribute to test creation.

Playwright MCP, integrated with LLMs, addresses these pain points by enabling natural language-driven testing, where you describe test scenarios in plain text, and the AI translates them into executable API calls. This approach democratizes testing, boosts productivity, and aligns with the shift toward AI-driven development workflows.

What is Playwright MCP?

Playwright MCP (Model Context Protocol) is an open-source server that bridges LLMs (like Claude, ChatGPT, or Gemini) with Playwright, a robust browser and API automation library built by ExecuteAutomation.

Playwright MCP server acts as a middleware that interprets natural language instructions and executes them as Playwright API requests.

Key features for API automation include:

• Support for HTTP Methods: GET, POST, PUT, PATCH, and DELETE requests.
• Plain English Commands: Write test cases without coding, e.g., “Perform a POST operation with this payload and verify the response.”
• Structured Responses: Leverages Playwright’s accessibility tree for UI testing and structured JSON for API testing, ensuring deterministic results.
• Integration with IDEs: Works seamlessly with tools like VS Code, Claude Desktop, and Cursor IDE.

How Playwright MCP + LLMs Work for API Automation

The magic of Playwright MCP lies in its ability to connect an LLM’s natural language processing capabilities with Playwright’s API testing prowess. Here’s how it works:

1. LLM Interpretation: You input a test scenario in plain English (e.g., “Send a GET request to this URL and check if the status is 200”).
2. MCP Server Translation: The Playwright MCP server parses the instruction and maps it to Playwright’s API methods.
3. Execution: Playwright sends the HTTP request, captures the response, and validates it against the specified conditions.
4. Feedback Loop: The LLM receives the results and can iterate on the test if needed, refining the scenario or handling dynamic responses.

Setting Up Playwright MCP for API Automation

Let’s walk through setting up the Playwright MCP server for API testing, based on the ExecuteAutomation repository.

Prerequisites

• Node.js: Install the LTS version.
• npm/npx: For package management.
• Claude Desktop Client or VS Code: For running the MCP server.
• A compatible browser (Chromium, Firefox, or WebKit) for UI testing (optional for API-only workflows).

Installation Steps

Install Playwright MCP Server:

npm install -g @executeautomation/playwright-mcp-server

This installs the server globally, enabling MCP functionality.

Configure Claude Desktop:

In the below screenshot you can see we have configure executeautomation playwright_mcp server

Edit the claude_desktop_config.json file in your Claude Desktop Client directory to include the below configration.

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["-y", "@executeautomation/playwright-mcp-server"]
    }
  }
}

Launch Claude Desktop Client:

In the below screenshot you can see once MCP server configration done Playwright MCP Server tools available. CURD methods are avibale for to do the API

Writing API Tests with Playwright MCP

Let’s explore a practical example of API automation using Playwright MCP. Lets takes an example of https://api.restful-api.dev/objects

GET Request

Send GET request with end pointhttps://api.restful-api.dev/objects to fetch all objects and Validate response
Check if status is 200
Ensure response body is an array

Output

In the below screenshot you can see status 200 and

POST request

Perform POST operation for the url https://api.restful-api.dev/objects

Pass below data into body 

{
   "name": "Apple MacBook M4 Pro 16",
   "data": {
      "year": 2025,
      "price": 6849.99,
      "CPU model": "Intel Core i12",
      "Hard disk size": "1 TB"
   }
}

Verify the response body contain id and createdAt 
and Saved the generated id in variable id

Output

In the below screenshot you can see status 200 and response body contain id and createdAt and Saved the generated id in variable id.

GET request For Saved ID

Now do the GET request for above Save id

Output

In the below screenshot you can see id is automatically passed and correct data is retrieve as per the given id

PUT request

In the PUT request we are updating data with passing the saved id

Perform PUT operation with using save id  @https://api.restful-api.dev/objects/id

Pass below data into body 

{
   "name": "Apple MacBook Pro 89",
   "data": {
      "year": 2025,
      "price": 123.99,
      "CPU model": "Intel Core i11",
      "Hard disk size": "10TB"
   }
}

Output

In the below screenshot you can see data is updated with correct saved id

In the below image you can see MCP server also provide the data before and after update.

DELETE request

In the DELETE request we are deleting data with passing the saved id

In the below screenshot you can see record is deleted

In the below screenshot you can see the final summary of CRUD operation.

Caution Points to Keep in Mind While Using LLM

While the combination of LLMs and Playwright MCP is powerful, a few considerations are essential for long-term success:

1. LLM Output Validation: Always validate LLM-generated tests. While Claude is highly capable, outputs may occasionally contain incorrect assumptions or edge case gaps.
2. Data Sensitivity: Avoid exposing sensitive production data when using LLMs or connecting to external services. Use mocks or sanitized test data wherever possible.
3. Version Control: Maintain proper versioning of test cases generated by LLMs to prevent drift between expected and actual API behaviors over time.
4. Human Oversight: Treat AI as a co-pilot, not an autopilot. Manual review and domain understanding remain critical for high-stakes API scenarios.
5. Dependency on External Models: Be aware of network reliability or access issues if your LLMs (e.g., Claude) are cloud-based. Have fallback strategies in place.

Conclusion

API testing is undergoing a remarkable transformation, and tools like Playwright MCP paired with LLMs such as Claude are leading the charge. By eliminating the need for boilerplate code and allowing testers to write test scenarios in natural language, this approach dramatically improves both speed and maintainability.