MCP Playwright Test

MCP Server

Automated UI and API testing powered by Playwright and MCP

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Updated Jul 10, 2025

About

MCP Playwright Test is a Model Context Protocol server that automates end‑to‑end testing for web applications. It clones or loads local code, generates test cases from requirements or API specs, runs Playwright UI and API tests, and produces detailed reports.

Capabilities

Resources

Access data sources

Tools

Execute functions

Prompts

Pre-built templates

Sampling

AI model interactions

Overview

MCP Playwright Test is a Model Context Protocol (MCP) server that bridges AI assistants with automated web and API testing powered by Playwright. It resolves a common pain point for developers working in AI‑augmented environments: the need to run end‑to‑end tests on freshly cloned or locally available codebases without leaving their AI tool (Claude Desktop, Cursor, etc.). By exposing a set of high‑level tools—such as repository cloning, browser launching, and test case generation—the server lets the AI generate, execute, and report on tests directly from natural language prompts.

The core value lies in its context‑aware automation. The server can pull the latest source from a Git repository or use an existing local directory, set up Playwright’s browser environment (chromium by default), and then produce test suites that reflect the user’s textual requirements. For API testing, it parses OpenAPI, Swagger, or Apifox specifications and auto‑generates request/response assertions. This eliminates manual boilerplate, ensures that tests stay in sync with the codebase, and enables rapid iteration when new features or bug fixes are introduced.

Key capabilities include:

Dynamic code acquisition: Clone a repository with configurable branch, depth, and authentication or use an existing local path.
Test case generation: Convert natural language descriptions into Playwright test scripts, or generate API tests from specification documents.
Dual‑mode execution: Run UI tests, API tests, or both in a single workflow, with optional headless mode and browser choice.
Comprehensive reporting: Capture network traffic, console logs, and produce detailed reports accessible via the resource.
Resource accessibility: Retrieve generated test cases () and execution reports through dedicated MCP resources.

Typical use cases span several real‑world scenarios:

Scenario	How MCP Playwright Test Helps
Continuous Integration	An AI assistant can trigger a test run on the latest commit, auto‑generate missing tests, and return a report to the CI pipeline.
Rapid Prototyping	A developer describes a new feature in plain English; the AI generates Playwright UI tests that validate the prototype before code review.
Regression Testing	On a new release, the AI pulls the main branch, executes all UI and API tests, and flags any failures in a single report.
API Documentation Validation	As part of documentation updates, the AI converts an OpenAPI spec into executable tests that confirm the API’s behavior matches its description.

Integration with AI workflows is seamless: the server exposes tools and resources that can be invoked through MCP prompts. A user simply asks, “Generate UI tests for the login page and run them.” The assistant calls with the appropriate description, then triggers , and finally fetches a report via the resource. This end‑to‑end loop keeps developers focused on high‑level design while the server handles the heavy lifting of test orchestration.

Unique advantages include its flexible configuration—via or environment variables—to support both local and remote projects, and its ability to auto‑detect API spec formats. The server’s design also accommodates AI tools that rely on MCP, making it a drop‑in solution for any workflow that needs rapid, repeatable testing without manual scripting.