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mcp-browser-use

MCP.Pizza Chef: JovaniPink

mcp-browser-use is a FastAPI-based MCP server that integrates the browser-use library to enable natural language-driven browser automation. It allows AI agents to control web browsers by interpreting natural language commands for navigation, interaction, and data extraction. Built on the Model Context Protocol, it facilitates seamless, real-time browser control for AI-enhanced workflows and automation tasks.

Use This MCP server To

Automate web navigation using natural language commands Extract structured data from websites via browser automation Perform multi-step web interactions for task completion Control browser sessions programmatically through MCP Enable AI agents to interact with web pages dynamically Automate form filling and submission on websites Scrape web content with contextual understanding Integrate browser automation into AI workflows and agents

README

MCP server w/ Browser Use

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MCP server for browser-use.

Browser Use Server MCP server

Overview

This repository contains the server for the browser-use library, which provides a powerful browser automation system that enables AI agents to interact with web browsers through natural language. The server is built on Anthropic's Model Context Protocol (MCP) and provides a seamless integration with the browser-use library.

Features

  1. Browser Control
  • Automated browser interactions via natural language
  • Navigation, form filling, clicking, and scrolling capabilities
  • Tab management and screenshot functionality
  • Cookie and state management
  1. Agent System
  • Custom agent implementation in custom_agent.py
  • Vision-based element detection
  • Structured JSON responses for actions
  • Message history management and summarization
  1. Configuration
  • Environment-based configuration for API keys and settings
  • Chrome browser settings (debugging port, persistence)
  • Model provider selection and parameters

Dependencies

This project relies on the following Python packages:

Package Version Description
Pillow >=10.1.0 Python Imaging Library (PIL) fork that adds image processing capabilities to your Python interpreter.
browser-use ==0.1.19 A powerful browser automation system that enables AI agents to interact with web browsers through natural language. The core library that powers this project's browser automation capabilities.
fastapi >=0.115.6 Modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. Used to create the server that exposes the agent's functionality.
fastmcp >=0.4.1 A framework that wraps FastAPI for building MCP (Model Context Protocol) servers.
instructor >=1.7.2 Library for structured output prompting and validation with OpenAI models. Enables extracting structured data from model responses.
langchain >=0.3.14 Framework for developing applications with large language models (LLMs). Provides tools for chaining together different language model components and interacting with various APIs and data sources.
langchain-google-genai >=2.1.1 LangChain integration for Google GenAI models, enabling the use of Google's generative AI capabilities within the LangChain framework.
langchain-openai >=0.2.14 LangChain integrations with OpenAI's models. Enables using OpenAI models (like GPT-4) within the LangChain framework. Used in this project for interacting with OpenAI's language and vision models.
langchain-ollama >=0.2.2 Langchain integration for Ollama, enabling local execution of LLMs.
openai >=1.59.5 Official Python client library for the OpenAI API. Used to interact directly with OpenAI's models (if needed, in addition to LangChain).
python-dotenv >=1.0.1 Reads key-value pairs from a .env file and sets them as environment variables. Simplifies local development and configuration management.
pydantic >=2.10.5 Data validation and settings management using Python type annotations. Provides runtime enforcement of types and automatic model creation. Essential for defining structured data models in the agent.
pyperclip >=1.9.0 Cross-platform Python module for copy and paste clipboard functions.
uvicorn >=0.22.0 ASGI web server implementation for Python. Used to serve the FastAPI application.

Components

Resources

The server implements a browser automation system with:

  • Integration with browser-use library for advanced browser control
  • Custom browser automation capabilities
  • Agent-based interaction system with vision capabilities
  • Persistent state management
  • Customizable model settings

Requirements

  • Operating Systems (Linux, macOS, Windows; we haven't tested for Docker or Microsoft WSL)
  • Python 3.11 or higher
  • uv (fast Python package installer)
  • Chrome/Chromium browser
  • Claude Desktop

Quick Start

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Installing via Smithery

To install Browser Use for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @JovaniPink/mcp-browser-use --client claude
Development Configuration
"mcpServers": {
  "mcp_server_browser_use": {
    "command": "uvx",
    "args": [
      "mcp-server-browser-use",
    ],
    "env": {
      "OPENAI_ENDPOINT": "https://api.openai.com/v1",
      "OPENAI_API_KEY": "",
      "ANTHROPIC_API_KEY": "",
      "GOOGLE_API_KEY": "",
      "AZURE_OPENAI_ENDPOINT": "",
      "AZURE_OPENAI_API_KEY": "",
      // "DEEPSEEK_ENDPOINT": "https://api.deepseek.com",
      // "DEEPSEEK_API_KEY": "",
      // Set to false to disable anonymized telemetry
      "ANONYMIZED_TELEMETRY": "false",
      // Chrome settings
      "CHROME_PATH": "",
      "CHROME_USER_DATA": "",
      "CHROME_DEBUGGING_PORT": "9222",
      "CHROME_DEBUGGING_HOST": "localhost",
      // Set to true to keep browser open between AI tasks
      "CHROME_PERSISTENT_SESSION": "false",
      // Model settings
      "MCP_MODEL_PROVIDER": "anthropic",
      "MCP_MODEL_NAME": "claude-3-5-sonnet-20241022",
      "MCP_TEMPERATURE": "0.3",
      "MCP_MAX_STEPS": "30",
      "MCP_USE_VISION": "true",
      "MCP_MAX_ACTIONS_PER_STEP": "5",
      "MCP_TOOL_CALL_IN_CONTENT": "true"
    }
  }
}

Environment Variables

Key environment variables:

# API Keys
ANTHROPIC_API_KEY=anthropic_key

# Chrome Configuration
# Optional: Path to Chrome executable
CHROME_PATH=/path/to/chrome
# Optional: Chrome user data directory
CHROME_USER_DATA=/path/to/user/data
# Default: 9222
CHROME_DEBUGGING_PORT=9222
# Default: localhost
CHROME_DEBUGGING_HOST=localhost
# Keep browser open between tasks
CHROME_PERSISTENT_SESSION=false

# Model Settings
# Options: anthropic, openai, azure, deepseek
MCP_MODEL_PROVIDER=anthropic
# Model name
MCP_MODEL_NAME=claude-3-5-sonnet-20241022
MCP_TEMPERATURE=0.3
MCP_MAX_STEPS=30
MCP_USE_VISION=true
MCP_MAX_ACTIONS_PER_STEP=5

Development

Setup

  1. Clone the repository:
git clone https://github.com/JovaniPink/mcp-browser-use.git
cd mcp-browser-use
  1. Create and activate virtual environment:
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Install dependencies:
uv sync
  1. Start the server
uv run mcp-browser-use

Debugging

For debugging, use the MCP Inspector:

npx @modelcontextprotocol/inspector uv --directory /path/to/project run mcp-server-browser-use

The Inspector will display a URL for the debugging interface.

Browser Actions

The server supports various browser actions through natural language:

  • Navigation: Go to URLs, back/forward, refresh
  • Interaction: Click, type, scroll, hover
  • Forms: Fill forms, submit, select options
  • State: Get page content, take screenshots
  • Tabs: Create, close, switch between tabs
  • Vision: Find elements by visual appearance
  • Cookies & Storage: Manage browser state

Security

I want to note that their are some Chrome settings that are set to allow for the browser to be controlled by the server. This is a security risk and should be used with caution. The server is not intended to be used in a production environment.

Security Details: SECURITY.MD

Contributing

We welcome contributions to this project. Please follow these steps:

  1. Fork this repository.
  2. Create your feature branch: git checkout -b my-new-feature.
  3. Commit your changes: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin my-new-feature.
  5. Submit a pull request.

For major changes, open an issue first to discuss what you would like to change. Please update tests as appropriate to reflect any changes made.

mcp-browser-use FAQ

How do I deploy the mcp-browser-use server?
Deploy it as a FastAPI application; ensure Python and dependencies are installed, then run the server script.
Can mcp-browser-use handle multi-tab or multi-window browsing?
Yes, it supports managing multiple tabs and windows through natural language commands.
Is authentication required to use the mcp-browser-use server?
Authentication depends on your deployment setup; the server itself does not enforce auth by default.
How does mcp-browser-use integrate with AI models?
It uses the MCP protocol to expose browser automation capabilities to AI models like OpenAI, Anthropic Claude, and Google Gemini.
What browsers are supported by mcp-browser-use?
It supports major browsers compatible with the browser-use library, typically Chromium-based browsers and Firefox.
Can I customize browser automation commands?
Yes, you can extend or customize commands by modifying the underlying browser-use library or server handlers.
How does mcp-browser-use ensure safe browser automation?
It scopes commands via MCP protocol, allowing controlled, observable interactions to prevent unsafe operations.
Does mcp-browser-use support headless browser operation?
Yes, it can run browsers in headless mode for automated workflows without UI.