Puppeteer MCP Server

The Merajmehrabi Puppeteer MCP Server bridges the gap between AI assistants and real‑world web interactions by exposing a rich set of browser automation tools over the Model Context Protocol. Rather than forcing developers to embed headless browsers directly into their applications, this server offers a clean, reusable interface that AI agents can invoke to perform tasks such as navigation, form submission, or data extraction. The result is a decoupled architecture where the heavy lifting of browser management lives in a dedicated service, keeping client code lightweight and focused on higher‑level logic.

At its core, the server launches or connects to Chrome instances using Puppeteer. In standard mode it spawns a fresh, isolated browser process, ensuring that each AI session starts with a clean slate. For scenarios where an existing Chrome window must be leveraged—perhaps to preserve user‑logged sessions or maintain a persistent state—the server supports active tab mode. By launching Chrome with the flag, developers can point the MCP tool to a running instance without disrupting it. The server then intelligently locates non‑extension tabs, connects through the debugging protocol, and hands control to the AI assistant. This duality gives teams flexibility: they can choose isolation for security or continuity for convenience.

The toolset is intentionally expressive yet straightforward. Commands such as , , and mirror common browser actions, while more advanced operations like expose the full power of JavaScript execution. Each tool accepts simple JSON payloads, making integration with language models trivial: an assistant can generate a structured prompt that the MCP server interprets without additional parsing logic. Screenshots, element hovering, and dropdown selection are all first‑class citizens, allowing AI agents to perform visual validations or complete multi‑step workflows that would otherwise require manual intervention.

Real‑world use cases abound. QA engineers can automate end‑to‑end tests by having an AI assistant navigate a site, submit forms, and capture screenshots for regression reports. Data scientists might scrape dynamic content from web dashboards by instructing the server to evaluate custom scripts that extract JSON blobs. Marketing teams could automate content posting across multiple platforms by having the assistant click through OAuth flows and submit articles. In all these scenarios, the server’s logging infrastructure (Winston with daily rotation) provides traceability and aids debugging when a step fails.

Finally, the server’s design emphasizes security and reliability. Remote debugging is only enabled on trusted networks, with guidelines for unique ports and timely shutdowns to mitigate exposure. Logging levels—from DEBUG to ERROR—allow developers to tune verbosity for development or production environments. Together, these features make the Merajmehrabi Puppeteer MCP Server a robust, developer‑friendly gateway for AI assistants that need to interact with the web in a controlled, reproducible manner.