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

Open Claude for Desktop
Go to Settings → Experimental features
Enable Claude API Beta and OpenAPI schema for API
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"]
        }
    }
}

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

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

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

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