Browser Control MCP Server

@adityasasidhar

代码仓库

基本信息

分类

浏览器自动化

传输方式

stdio

作者

adityasasidhar

提交者

Aditya Sasidhar

关于 Browser Control MCP Server

Stop feeding your agent raw HTML. BrowserControl gives LLMs true visual grounding with Set of Marks (SoM), letting them click exactly what they see without hallucinating selectors. With built-in developer tools, session recording, and persistent logins, it transforms web automati

#browser-control#browser-automation #developer-tools

Browser Control MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "browsercontrol": {
      "command": "python",
      "args": [
        "-m",
        "browsercontrol"
      ]
    }
  }
}

Browser Control MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Browser Control?

Browser Control is a production-ready MCP server that gives AI agents real browser capabilities using vision-first interaction. It controls Chromium through Playwright, providing screenshots with numbered interactive elements, persistent sessions, and built-in developer tooling—all locally without external vision APIs.

How to use Browser Control?

Install Python 3.11+ and Playwright (Chromium installed automatically). Add Browser Control as an MCP server in your AI agent’s client. Agents then call tools like click(5) or type_text(3, "hello") to interact with web pages.

Key features of Browser Control

Vision-first interaction via annotated page screenshots
Set of Marks (SoM) with numbered interactive elements
Persistent browser sessions (cookies, login, history)
Built-in developer tools (console, network, JS execution)
Session recording with Playwright traces
Zero extra AI cost (no vision models or selector inference)

Use cases of Browser Control

Autonomous web research and data extraction
Browser-based agent workflows requiring login persistence
Automated form filling and end-to-end testing
Debugging web apps via console logs and network requests

常见问题

How does Browser Control differ from DOM-based tools?

It uses vision-first interaction based on page screenshots, avoiding fragile DOM queries. This reduces tokens per action by 50–100× and eliminates brittle selector inference.

What are the runtime dependencies?

Python 3.11+, FastMCP, and Playwright (auto-installs Chromium). All execution is local—no external AI or API calls.

Where does browser state and session data live?

All data, including cookies, localStorage, and recording traces, is stored locally on the machine running the MCP server.

What transport does Browser Control use?

It uses the MCP (Model Context Protocol) transport, designed for integration with AI agent clients. No authentication details are specified in the documentation.

Are there any known limits or stability notes?

Browser Control is stable, actively developed, and MIT licensed. No specific limits are documented beyond requiring a local Chromium installation.

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