Chrome Server MCP – Browser Automation for AI Assistants

The Chrome Server MCP is a specialized Model Context Protocol server that gives Claude‑powered assistants, such as those in Cursor’s editor, direct control over a Chrome browser instance. By bridging the gap between an AI’s natural‑language commands and low‑level browser actions, it enables developers to orchestrate complex web interactions—navigation, form filling, element clicking, and screenshotting—without leaving the AI environment. This capability is especially valuable for tasks that require real‑time web data extraction, automated testing, or dynamic content manipulation.

At its core, the server exposes a set of high‑level browser commands over WebSocket. When an AI assistant issues a prompt like “Open the login page and submit my credentials,” the MCP translates that into a sequence of browser operations: navigating to a URL, typing text into input fields, clicking the submit button, and capturing any resulting screenshot. The browser extension receives these instructions via a persistent WebSocket channel on port 9876, ensuring low‑latency, bidirectional communication. This design keeps the AI logic decoupled from the browser’s execution context while maintaining a seamless user experience.

Key features include:

• Full Browser Automation – Programmatically control navigation, clicks, typing, and keyboard events.
• Screenshot Capture – Grab full‑page or element‑specific images for visual validation or reporting.
• Multi‑Tab Management – Create, list, switch, and close tabs to parallelize browsing tasks.
• Batch Commands – Chain multiple operations into a single, atomic request for efficiency.
• Viewport Control – Adjust window size to test responsive layouts or simulate different devices.

Real‑world scenarios benefit from this integration: automated data scraping for research, end‑to‑end UI testing driven by natural language scripts, or interactive tutorials that guide users through web workflows. Developers can embed the MCP server into their CI pipelines, enabling AI assistants to trigger browser actions as part of deployment checks. Because the server is built on Node.js and communicates via a simple WebSocket interface, it can be easily extended or combined with other MCP tools to create sophisticated AI‑driven web automation stacks.