MCP-Puppeteer Server

The Puppeteer MCP Server brings the power of headless browser automation directly into AI‑centric workflows. By exposing a rich set of Puppeteer‑based actions—navigation, form filling, screenshotting, JavaScript execution and more—as MCP tools, it allows language models like Claude to interact with live web pages in a structured, repeatable way. This eliminates the need for developers to write custom browser scripts or maintain separate automation pipelines, letting them focus on higher‑level logic while the server handles all browser orchestration.

At its core, the server can launch fresh Chromium instances or attach to existing Chrome windows. This dual capability is especially valuable in scenarios where stateful browsing is required: a model can log into an account, navigate through multiple pages, and then hand off control to another tool or a subsequent conversation without losing session context. The “smart tab management” feature ensures that the server reuses open tabs when appropriate, reducing resource consumption and speeding up repeated tasks.

Key capabilities are grouped into intuitive tool definitions: Navigate, Click, FillForm, SelectOption, Hover, and ExecuteJS. Each tool accepts a concise JSON payload, enabling models to describe complex interactions in natural language that the server translates into precise Puppeteer commands. The screenshot tool is particularly useful for visual validation or generating image assets on demand, while the JavaScript executor allows models to query page state or manipulate the DOM in ways that go beyond standard form interactions.

Typical use cases span automated testing, data extraction, and content generation. For example, a QA engineer can ask the model to “fill out the signup form on example.com and capture the confirmation page”, while a content creator might request a series of screenshots from different product pages to assemble into a marketing deck. In CI/CD pipelines, the server can run end‑to‑end tests or scrape metrics from internal dashboards without manual intervention.

Integration with existing MCP workflows is seamless. Once the server is registered in a Claude configuration file, any conversation can invoke its tools by referencing their names. Because the server follows MCP conventions, developers can compose tool chains—combining navigation, data extraction, and post‑processing—within a single prompt. The result is a declarative, reproducible automation layer that scales with the complexity of the task and the sophistication of the language model.