Browser Use MCP Server

How to use

To use this MCP server, you start the server with a command that launches the browser automation backend and then connect your MCP client (such as an AI agent or a testing script) to it. You can run two core capabilities: (1) automate a browser task end-to-end using natural language instructions, and (2) perform deep web research and generate a structured report. Your environment variables define which LLMs you use, how the browser runs, and where artifacts are stored. When you issue tasks through your MCP client, the server handles navigation, form interactions, and data extraction, returning results or detailed errors.

Examples of practical usage patterns include: composing a sequence of browser actions to gather information from multiple pages, running a research task that compiles a multi-page report, and optionally keeping a browser session alive across multiple MCP calls for faster interactions. If you choose to connect to your own browser via CDP, you can reuse an already-running Chrome/Chromium instance that you launch yourself.

How to install

Prerequisites you need before installing: a Python-enabled runtime, a modern node-compatible toolchain, and a supported browser automation runtime. You will also need network access to fetch LLM model keys and any required API credentials.

Step 1: Install the UV bootstrapper for Python environments. Run the following command to install UV, the high-speed installer:

curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install Playwright browsers, which are required for browser automation. Run:

uvx --from mcp-server-browser-use@latest python -m playwright install

Configuration and MCP server setup

Configure how the MCP server runs using environment variables. The key sections define the LLM provider, browser settings, agent tooling behavior, and research tooling. You will often place these settings in a .env file and load them when starting the server.

{
  "mcpServers": {
    "browser_use": {
      "command": "uvx",
      "args": ["mcp-server-browser-use@latest"],
      "env": {
        "MCP_LLM_OPENROUTER_API_KEY": "YOUR_KEY_HERE_IF_USING_OPENROUTER",
        "MCP_LLM_PROVIDER": "openrouter",
        "MCP_LLM_MODEL_NAME": "anthropic/claude-3.5-haiku",
        "MCP_BROWSER_HEADLESS": "false",
        "MCP_BROWSER_USE_OWN_BROWSER": "false",
        "MCP_BROWSER_WINDOW_WIDTH": "1440",
        "MCP_BROWSER_WINDOW_HEIGHT": "1080",
        "MCP_AGENT_TOOL_HISTORY_PATH": "/path/to/your/history",
        "MCP_RESEARCH_TOOL_SAVE_DIR": "/path/to/your/research",
        "MCP_RESEARCH_TOOL_MAX_PARALLEL_BROWSERS": "5",
        "MCP_PATHS_DOWNLOADS": "/path/to/your/downloads",
        "MCP_SERVER_LOGGING_LEVEL": "DEBUG",
        "MCP_SERVER_LOG_FILE": "/path/to/your/log/mcp_server_browser_use.log",
        "MCP_LLM_GOOGLE_API_KEY": "YOUR_GOOGLE_API_KEY_IF_USING_GOOGLE",
        "MCP_LLM_OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_IF_USING_OPENAI",
        "MCP_LLM_AZURE_OPENAI_API_KEY": "YOUR_AZURE_OPENAI_API_KEY",
        "MCP_LLM_AZURE_OPENAI_ENDPOINT": "https://your-azure-endpoint/",
        "MCP_LLM_OPENROUTER_API_KEY": "YOUR_OPENROUTER_KEY"
      }
    }
  }
}

Connecting to your own browser (CDP)

You can attach the MCP server to a Chrome/Chromium browser you launch yourself with remote debugging enabled. Start Chrome with a remote debugging port, then point the server to that port via CDP URL.

Steps to connect your own browser:

CLI usage and testing

A command-line interface lets you test core capabilities directly from your terminal. You can run a browser task or a deep research task without building a full client. Use the CLI to iterate quickly during development and scripting.

Troubleshooting and notes

If the server fails to start due to a missing setting, ensure all mandatory environment variables are defined in your environment or loaded from your .env file. If you are not using CDP, verify there are no conflicting browser instances using the same data directory. For connectivity issues with CDP, confirm the port matches and that the browser is accessible. Validate API keys and endpoints for all LLM providers. If vision features are enabled, ensure your LLM supports vision.

Additional notes

Development and testing guidance covers syncing dependencies, installing browser automation runtimes, and using the inspector to connect to a running browser. You can run a local development flow that starts the MCP server with a specific dev path and then run browser tasks or deep research from the CLI.

run_browser_agent

Executes a browser automation task based on natural language instructions and waits for it to complete. It uses settings from MCP_AGENT_TOOL_* and MCP_BROWSER_* environment variables and can save optional agent history.

run_deep_research

Performs in-depth web research on a topic, generates a Markdown report, and saves outputs if a save directory is configured.