MCP server w/ Browser Use

MCP server for browser-use.

<a href="https://glama.ai/mcp/servers/tjea5rgnbv"><img width="380" height="200" src="https://glama.ai/mcp/servers/tjea5rgnbv/badge" alt="Browser Use Server MCP server" /></a>

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

2. Agent System

• Custom agent implementation in custom_agent.py
• Vision-based element detection
• Structured JSON responses for actions
• Message history management and summarization

3. 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

<details> <summary>Development Configuration</summary>

"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"
    }
  }
}

</details>

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

2. Create and activate virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

uv sync

4. 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.