Browser Use MCP Server

A FastMCP server that enables browser automation through natural language commands. This server allows Language Models to browse the web, fill out forms, click buttons, and perform other web-based tasks via a simple API.

Quick Start

1. Install the package

Install with a specific provider (e.g., OpenAI)

pip install -e "git+https://github.com/yourusername/browser-use-mcp.git#egg=browser-use-mcp[openai]"

Or install all providers

pip install -e "git+https://github.com/yourusername/browser-use-mcp.git#egg=browser-use-mcp[all-providers]"

Install Playwright browsers

playwright install chromium

2. Configure your MCP client

Add the browser-use-mcp server to your MCP client configuration:

{
    "mcpServers": {
        "browser-use-mcp": {
            "command": "browser-use-mcp",
            "args": ["--model", "gpt-4o"],
            "env": {
                "OPENAI_API_KEY": "your-openai-api-key",  // Or any other provider's API key
                "DISPLAY": ":0"  // For GUI environments
            }
        }
    }
}

Replace "your-openai-api-key" with your actual API key or use an environment variable reference like process.env.OPENAI_API_KEY.

3. Use it with your favorite MCP client

Example using mcp-use with Python

import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient

async def main():
    # Load environment variables
    load_dotenv()

    # Create MCPClient from config file
    client = MCPClient(
        config={
            "mcpServers": {
                "browser-use-mcp": {
                    "command": "browser-use-mcp",
                    "args": ["--model", "gpt-4o"],
                    "env": {
                        "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"),
                        "DISPLAY": ":0",
                    },
                }
            }
        }
    )

    # Create LLM
    llm = ChatOpenAI(model="gpt-4o")

    # Create agent with the client
    agent = MCPAgent(llm=llm, client=client, max_steps=30)

    # Run the query
    result = await agent.run(
        """
        Navigate to https://github.com, search for "browser-use-mcp", and summarize the project.
        """,
        max_steps=30,
    )
    print(f"\nResult: {result}")

if __name__ == "__main__":
    asyncio.run(main())

Using Claude for Desktop

1. Open Claude for Desktop
2. Go to Settings → Experimental features
3. Enable Claude API Beta and OpenAPI schema for API
4. Add the following configuration to your Claude Desktop config file:
   • Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %AppData%\Claude\claude_desktop_config.json

{
    "mcpServers": {
        "browser-use": {
            "command": "browser-use-mcp",
            "args": ["--model", "claude-3-opus-20240229"]
        }
    }
}

5. Start a new conversation with Claude and ask it to perform web tasks

Supported LLM Providers

The following LLM providers are supported for browser automation:

Provider	API Key Environment Variable
OpenAI	OPENAI_API_KEY
Anthropic	ANTHROPIC_API_KEY
Google	GOOGLE_API_KEY
Cohere	COHERE_API_KEY
Mistral AI	MISTRAL_API_KEY
Groq	GROQ_API_KEY
Together AI	TOGETHER_API_KEY
AWS Bedrock	AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
Fireworks	FIREWORKS_API_KEY
Azure OpenAI	AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT
Vertex AI	GOOGLE_APPLICATION_CREDENTIALS
NVIDIA	NVIDIA_API_KEY
AI21	AI21_API_KEY
Databricks	DATABRICKS_HOST and DATABRICKS_TOKEN
IBM watsonx.ai	WATSONX_API_KEY
xAI	XAI_API_KEY
Upstage	UPSTAGE_API_KEY
Hugging Face	HUGGINGFACE_API_KEY
Ollama	OLLAMA_BASE_URL
Llama.cpp	LLAMA_CPP_SERVER_URL

For more information check out: https://python.langchain.com/docs/integrations/chat/

You can create a .env file in the project directory with your API keys:

OPENAI_API_KEY=your_openai_key_here
# Or any other provider key

Troubleshooting

• API Key Issues: Ensure your API key is correctly set in your environment variables or .env file.
• Provider Not Found: Make sure you've installed the required provider package.
• Browser Automation Errors: Check that Playwright is correctly installed with playwright install chromium.
• Model Selection: If you get errors about an invalid model, try using the --model flag to specify a valid model for your provider.
• Debug Mode: Use --debug to enable more detailed logging that can help identify issues.
• MCP Client Configuration: Make sure your MCP client is correctly configured with the right command and environment variables.

License

MIT # browser-use-mcp