Playwright Test Automation Framework with MCP Integration

This MCP server extends a conventional Playwright‑based test harness by adding Model Context Protocol (MCP) support for recording, playback, and automated test script generation. The server exposes a rich set of resources that let AI assistants—such as Claude or other MCP‑enabled agents—interact directly with the test environment, query page state, and trigger actions in a declarative way. By bridging Playwright with MCP, the framework solves the problem of manual test script authoring and maintenance: developers can now ask an AI to generate or modify tests on the fly, record user interactions into reusable test cases, and replay those recordings with a single command.

What the Server Does

At its core, the MCP server listens for JSON‑encoded commands that mirror Playwright’s API surface. When an AI assistant sends a request to “click the submit button” or “capture the current URL,” the server translates that into a Playwright action, executes it in a browser context, and returns the result. This tight coupling allows AI agents to treat the test environment as a first‑class resource, enabling dynamic test creation, live debugging, and continuous integration workflows that are driven by natural language or high‑level specifications. The server also provides endpoints for test recording—capturing a sequence of interactions that can later be replayed or exported as code—making it straightforward to bootstrap new test scenarios from real user sessions.

Key Features Explained

• Declarative Test Interaction: AI clients can issue high‑level commands (e.g., “navigate to login page”, “fill username”) without needing to write Playwright code. The server handles the translation and execution.
• Recording & Playback: Users can record a series of steps in a browser, which the server captures as an MCP‑compatible script. These recordings can be replayed verbatim or transformed into TypeScript test files.
• Data‑Driven Architecture: Test data lives in separate JSON/TypeScript modules, allowing the AI to inject new scenarios by simply augmenting data files rather than altering logic.
• Page Object Model (POM) Integration: The framework’s POM design means that the MCP server can reference page objects, keeping locators and actions encapsulated while still exposing them to AI agents.
• TypeScript Support: Strong typing ensures that the MCP interface benefits from IntelliSense and compile‑time safety, which is crucial when AI tools generate or modify code.

Real‑World Use Cases

• Rapid Prototyping: QA engineers can ask an AI to “create a test for the new checkout flow” and receive a fully‑functional test script instantly.
• Continuous Regression Testing: Automated pipelines can use the MCP server to replay recorded user journeys whenever a new build is deployed, ensuring that critical paths remain intact.
• Assistive Development: Developers unfamiliar with Playwright can interact with the test suite through natural language commands, lowering the learning curve.
• Cross‑Device Testing: The server can orchestrate tests across multiple browser contexts, enabling AI to generate device‑specific scenarios without manual configuration.

Integration with AI Workflows

The MCP server plugs into existing AI pipelines by exposing a standard set of resources: , , and custom helpers for data handling. An AI assistant can, for example, query the current page title, capture screenshots, or iterate over data sets to generate multiple test variations—all through a single, consistent protocol. Because the server is built on top of Playwright’s robust API, it inherits all browser capabilities (headless/headed modes, network interception, performance metrics), allowing AI agents to craft sophisticated end‑to‑end tests that would otherwise require extensive manual scripting.

Unique Advantages

• Seamless AI‑First Development: By exposing Playwright actions as MCP resources, the framework turns a traditional testing tool into an interactive AI‑driven platform.
• Unified Recording Mechanism: The recording feature bridges the gap between manual exploratory testing and automated regression, giving teams a single source of truth for test cases.
• Scalable Architecture: The combination of POM, data‑driven design, and MCP integration ensures that adding new features or expanding test coverage requires minimal code changes—primarily updates to data files or AI prompts.

Overall, the Playwright Test Automation Framework with MCP integration empowers developers and QA teams to harness AI for rapid test creation, maintainability, and continuous delivery, turning manual testing bottlenecks into automated, AI‑driven workflows.