Playwright SSE MCP Server

The Playwright SSE MCP Server turns the powerful, browser‑automation library Playwright into a first‑class tool that can be invoked through the Model Context Protocol (MCP). By exposing Playwright’s API over a Server‑Sent Events (SSE) endpoint, the server allows AI assistants—such as Claude—to issue browser commands, capture screenshots, scrape data, or run end‑to‑end tests directly from within a conversational flow. This integration removes the need for developers to write custom connectors or handle complex authentication, letting them focus on higher‑level logic while the server manages all browser orchestration.

Why It Matters

Modern AI workflows often require interaction with web pages: validating UI changes, extracting dynamic content, or simulating user journeys. Traditionally, developers would embed Playwright scripts in separate test suites and trigger them manually or via CI pipelines. The MCP server consolidates this capability into a lightweight, container‑friendly service that can be started with a single Docker Compose command. This makes browser automation readily available to any MCP client, enabling real‑time, stateful interactions that were previously cumbersome or impossible in a purely conversational context.

Core Features

• SSE‑Based Streaming: The server communicates over SSE, allowing the AI client to receive incremental responses (e.g., progress updates or page content) without blocking. This is especially useful for long‑running actions like waiting for network idle or loading complex pages.
• Container Isolation: Built to run inside Docker, the server can be networked with other MCP services via a shared . This guarantees deterministic isolation and simplifies deployment in multi‑service architectures.
• Custom Port Configuration: Developers can override the default port (3002) through an environment variable, ensuring compatibility with existing infrastructure or avoiding conflicts.
• Cross‑Platform Access: Whether connecting from the host, another container on the same network, or a development container that doesn’t share the network, the README provides clear guidance on URL construction and DNS resolution (e.g., for Docker Desktop).

Real‑World Use Cases

• Live Testing from Conversational Agents: An AI assistant can prompt the user for a URL, then instruct the server to navigate, capture screenshots, and report back the results—all within the chat.
• Dynamic Data Extraction: When a user asks for the latest product prices, the assistant can use Playwright to scrape the e‑commerce site and return up‑to‑date information without exposing scraping logic to the user.
• Continuous Integration: CI pipelines can register the server as an MCP endpoint, allowing test scripts to be written once and reused across multiple environments (dev, staging, prod).
• Browser‑Based Debugging: Developers can invoke Playwright actions from within an IDE or a lightweight UI to debug front‑end issues on demand, with results streamed back in real time.

Integration Flow

1. Deploy the Server: Run (or use the provided shell scripts) to bring the server online on a chosen port.
2. Register in MCP Client: Add an entry to the client’s configuration pointing to .
3. Invoke Playwright Commands: The AI client sends structured requests (e.g., “navigate to ”) over the SSE channel; the server executes them and streams back responses.
4. Handle Results: The client can parse streamed events, display screenshots, or trigger subsequent actions based on the outcome.

By turning Playwright into an MCP‑compatible service, this server bridges the gap between AI assistants and real‑world web interactions, offering developers a seamless, scalable way to harness browser automation within conversational AI workflows.