Playwright Test Framework Example for AI & Playwright MCP

Overview

The Playwright Test Framework MCP Example is a ready‑made, highly modular test harness that bridges the Model Context Protocol (MCP) with Playwright’s end‑to‑end testing capabilities. It addresses a common pain point for AI‑augmented development workflows: the difficulty of orchestrating browser automation, API calls, and data‑driven tests while maintaining a clean, maintainable codebase. By packaging the most frequently used testing patterns—Page Object Model, fixture‑based page objects, and multi‑reporter output—into a single repository, the server lets AI assistants instantly spin up realistic test scenarios without the overhead of setting up boilerplate.

At its core, the MCP server exposes a set of resources that encapsulate Playwright actions (e.g., navigation, form submission, API requests) and tools that perform higher‑level tasks such as user registration or article retrieval. These abstractions are described in plain JSON, allowing an AI client to request a “register user” operation and receive a fully executed test case with minimal input. The server’s prompt templates guide the assistant to generate context‑aware test scripts, while the sampling component ensures deterministic responses for repeatable test runs. Developers can therefore ask an AI assistant to “create a UI test that logs in and views articles,” and the MCP server will translate that into a structured Playwright workflow.

Key capabilities include:

• Page Object Model (POM) integration: Each page is represented as a class, keeping selectors and actions isolated. The MCP server references these objects via tool definitions, enabling AI to compose tests that are both readable and resilient to UI changes.
• API‑first testing: Endpoints for health checks, user management, and article retrieval are exposed as MCP resources. This allows AI to generate tests that validate both the UI layer and the underlying API contract in a single pass.
• Custom fixtures & helpers: The server leverages Playwright’s fixture system to inject reusable page objects and authentication tokens. AI assistants can request a “logged‑in session” resource, which automatically provisions the necessary fixtures.
• Multi‑reporting: The configuration supports HTML, JUnit XML, and JSON reporters. AI can instruct the server to output a specific report format, facilitating integration with CI/CD pipelines or test management tools.

Real‑world use cases span from rapid prototyping of new features to regression testing in continuous integration environments. For example, a product owner can describe desired user flows to an AI assistant; the MCP server then produces Playwright tests that validate those flows against a live demo application. In another scenario, a quality engineer can ask the assistant to “add a test for article visibility when logged out,” and the server will generate, execute, and report the results instantly.

Integration into AI workflows is seamless: the MCP server runs as a local or cloud service, exposing its capabilities over HTTP. An AI assistant simply calls the appropriate endpoint with a structured request, receives back a test script or execution result, and can iterate on the prompt until the desired behavior is achieved. This tight coupling eliminates manual copy‑paste of test code, reduces human error, and accelerates the feedback loop between feature development and quality assurance.

In summary, this MCP example demonstrates how AI can be leveraged to automate the creation, execution, and reporting of Playwright tests. By abstracting common testing patterns into MCP resources and tools, it empowers developers to focus on business logic while the assistant handles the repetitive, boilerplate‑heavy aspects of test engineering.