Puppeteer MCP Server

Puppeteer MCP Server – Browser Automation for AI Assistants

The Puppeteer MCP server bridges the gap between large language models and real‑world web interactions. By exposing a suite of browser automation tools over the Model Context Protocol, it allows an LLM to control a headless Chromium instance as if it were a human user. This capability is essential for tasks that require real‑time data extraction, UI testing, or any scenario where the assistant must read from or write to live web pages.

At its core, the server offers a collection of intuitive tools that map directly to common browser actions. Developers can instruct the model to navigate to any URL, click or hover over elements, fill out forms, and even execute arbitrary JavaScript. Each tool accepts a small set of arguments—primarily CSS selectors or URLs—making the API straightforward to reason about while still powerful enough for complex workflows. For example, a user can ask the assistant to “search for ‘latest climate reports’ on Google, click the first result, and capture a screenshot of the article,” all through a single sequence of tool calls.

The server’s resource layer provides two convenient data streams: console logs and screenshots. Console logs () capture every message emitted by the page, enabling debugging or monitoring of client‑side scripts. Screenshots () are stored as PNG files and can be referenced by name, allowing the assistant to return visual evidence of its actions. These resources make it easy to incorporate visual feedback into conversational flows or automated reporting.

Real‑world use cases span from automated web scraping and data validation to end‑to‑end testing of web applications. QA engineers can script interactions that mimic real users, while data scientists might use the server to harvest dynamic content that static APIs cannot provide. In a customer‑support context, an assistant could open a support portal, fill out tickets, and confirm submission—all through MCP commands.

Integration is seamless for developers familiar with MCP. The server can be launched via Docker or NPX, and its tools are automatically discoverable by any MCP‑compliant client. Because the API remains stateless and declarative, it fits naturally into existing AI workflows that rely on tool calls, prompting, or chain‑of‑thought reasoning. The unique advantage of this server lies in its lightweight headless implementation coupled with a rich set of browser actions, giving AI assistants the ability to perform tasks that were previously limited to manual browsing or custom scripts.