AIAny
GitHub

mcp-server-browserbase

Exposes a managed cloud browser to an LLM as MCP tools, letting an agent open sessions, navigate, click, read page elements, and pull data from live websites. Built on Stagehand, so steps are written in plain language, not brittle CSS selectors.

Introduction

Most "give your agent a browser" tools break the moment a page re-renders or an ad shifts the DOM, because they hard-code CSS selectors that drift constantly. This MCP server takes a different bet: it routes every action through Stagehand's model-driven layer, so the agent says "click the login button" and the server figures out which element that is at runtime. The browser itself runs on Browserbase's cloud, not the agent's machine, which means proxies, stealth, and CAPTCHA handling come for free.

What Sets It Apart
  • Selectors are inferred, not declared. The agent describes intent in natural language; Stagehand resolves it against the live DOM, so the same script survives layout changes that would shatter a Playwright recording.
  • The browser is infrastructure, not a local process. Sessions live on managed cloud browsers with rotating proxies and verified-identity options, so anti-bot defenses that block local headless runs are handled outside the agent's logic.
  • Two ways to wire it in. A hosted SHTTP endpoint needs only an API key, while a self-hosted STDIO build lets you pin your own model (Gemini, GPT, or Claude) and keep traffic in-house.
适合谁 + 权衡

A strong fit if your agent must operate sites that have no API and change often, or if you want web automation without running a browser fleet yourself. Look elsewhere if your targets expose clean APIs, if you need deterministic, replayable scripts (model-inferred actions vary run to run), or if per-session cloud cost and the dependency on Browserbase's platform are dealbreakers.

Information

  • Websitegithub.com
  • AuthorsBrowserbase, Inc.
  • Published date2024/12/06

Categories

More Items

GitHub
AI Infra2026

Compresses tool outputs, logs, files and RAG chunks locally before they reach the LLM to reduce token usage while caching originals for on-demand retrieval. Provides proxy/library/wrap modes, JSON- and code-aware compressors, and reversible compression.

GitHub

Connects Blender to any LLM so models can inspect, create, and modify 3D scenes via a socket-based Model Context Protocol (MCP) server; supports object manipulation, materials, renders, asset import, and executing Blender Python.

GitHub
AI Infra2026

Indexes mixed-format folders locally into a zero-config, Elasticsearch-compatible search engine offering keyword, semantic, vector and hybrid retrieval. Embeds on ingest, provides per-agent namespaced memory and AST-aware code indexing in a single static binary with no external embedding key.