Give Your LLMs Real Browser Control

Stop wrestling with headless browser setups and flaky automation scripts. The Browserbase MCP Server puts cloud browsers directly under your LLM's control—no local Chrome installations, no Docker containers, no dependency hell.

Why This Changes Everything

You're building with LLMs, but they're trapped in text. Meanwhile, the web holds the data you need, the forms you want to fill, and the workflows you want to automate. Traditional web scraping breaks constantly. Selenium scripts are brittle. This MCP server bridges that gap with cloud browsers that just work.

Two Powerful Approaches

Browserbase MCP gives you granular control—perfect when you need precise automation:

// Navigate and extract structured data
await openUrl("https://example-ecommerce.com/products")
await clickElement(".search-button")
await typeText("input[name='query']", "red shoes")
const products = await extractText(".product-card")

Stagehand MCP uses atomic, natural language instructions—ideal for complex interactions:

// High-level commands that handle the complexity
await act("click the login button")
await act("fill out the contact form with John's details")
await extract("find all product prices on this page")

Real Productivity Gains

Data Collection: Build LLM assistants that gather competitor pricing, monitor job boards, or track product availability across multiple sites. No more manual CSV exports.

QA Automation: Your LLM can now test user flows, fill forms with edge case data, and screenshot issues—all from natural language instructions in your chat interface.

Research Workflows: Create AI assistants that navigate documentation sites, extract relevant code examples, and compile research findings without you clicking through dozens of pages.

Customer Support: Build bots that can actually help users by navigating their account dashboards, filling forms, and capturing screenshots of issues.

Built for LLM Integration

Unlike traditional browser automation tools built for developers, this MCP server speaks LLM. Commands like act("find the red shoes") work because it uses vision models to understand complex DOMs. No more fragile CSS selectors that break when sites update.

The atomic instruction model means your LLM can chain actions naturally:

1. "Navigate to the pricing page"
2. "Extract all plan features"
3. "Take a screenshot of the comparison table"
4. "Click the enterprise contact form"

Getting Started

npm install mcp-server-browserbase
export BROWSERBASE_API_KEY="your-key"
export STAGEHAND_API_KEY="your-key"
npx mcp-server-browserbase

Your browsers run in Browserbase's cloud infrastructure. No local setup, no resource drain on your machine, no browser profile management. Just reliable automation that scales.

Integration Reality

This plugs into your existing MCP-compatible LLM applications immediately. Whether you're building with Claude, GPT-4, or custom models, the standardized MCP interface means your browser automation just works.

The session management handles state persistence, so your LLM can navigate multi-step workflows without losing context. The screenshot capabilities give vision models the visual feedback they need for complex interactions.

With 1,925 stars and active development, this isn't experimental tooling—it's production-ready browser automation that finally makes LLM web interactions reliable.

Your LLMs are about to get a lot more useful.