Cloud Browser Automation via MCP (Integrated/Extended)
Overview
A Model Context Protocol (MCP) server that gives LLM applications cloud browser-automation capabilities through Browserbase and Stagehand. Built around the open MCP standard, it lets an AI client navigate pages, fill forms, click, screenshot, monitor console logs and extract structured data in a managed cloud browser. Our role was integrating and extending this open-source server.
Why It Exists
To give Claude and other LLM clients reliable, sandboxed access to the live web, an agent needs a standardised tool interface to a real browser. MCP provides the protocol; Browserbase provides the cloud browsers. This server bridges the two so any MCP-compatible client gains web automation as a set of tools.
What We Built
We worked with the dual-server monorepo: a browserbase/ MCP server (TypeScript, with src/, utils/, tests/, a cli.js, playwright.config.ts, a Dockerfile and a smithery.yaml for distribution) exposing browser automation, data extraction, console monitoring, screenshots and web interaction as MCP tools; and a stagehand/ server adding Stagehand’s higher-level act/extract/observe primitives. The work centres on integrating, configuring and deploying these MCP servers so LLM agents can drive cloud browsers, architecting around and extending the open-source project rather than authoring it from scratch.
Technologies & Approach
The Model Context Protocol for the standardised tool interface; Browserbase for managed, scalable cloud browsers; Stagehand + Playwright for the actual page control and natural-language actions; TypeScript throughout, with Docker and Smithery for packaging and distribution. MCP was chosen because it makes the browser capability portable across any compliant LLM client.
Outcome / Impact
Delivered a working, distributable bridge between LLM agents and cloud browsers, demonstrating fluency with the MCP standard, cloud-browser infrastructure and tool-enabled agent design.
Capabilities Demonstrated
- Developing and deploying MCP servers for LLM clients
- Cloud browser automation (Browserbase) for AI agents
- Tool-enabled agent actions: navigate, click, fill, screenshot, extract
- Integrating and extending open-source AI infrastructure