Browserbase MCP Server

What does “Browserbase” MCP Server do?

The Browserbase MCP Server allows Language Model-based AI assistants to control and automate browsers in the cloud using Browserbase and Stagehand . By leveraging the Model Context Protocol (MCP), this server enables LLMs to interact with web pages, perform browser automation tasks, extract data, take screenshots, monitor console logs, and execute JavaScript—all within a secure, cloud-based environment. This powerful capability enhances development workflows by enabling seamless automation of web-based tasks, integration with external web services, and standardized orchestration of browser-based workflows in AI-powered applications.

List of Prompts

No information about prompt templates is provided in the available files or documentation.

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List of Resources

No explicit list of MCP resources is provided in the available files or documentation.

List of Tools

No direct list of tools (e.g., from server.py or similar) is available in the README or visible repository structure.

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Use Cases of this MCP Server

• Browser Automation: Orchestrate and control cloud browsers for automated testing, scraping, or repetitive web actions.
• Data Extraction: Extract structured data from any webpage, which is useful for market research, price monitoring, or content aggregation.
• Console Monitoring: Track and analyze browser console logs for debugging or monitoring web application health.
• Screenshot Capture: Automate full-page and element-specific screenshots for UI regression testing, documentation, or monitoring.
• Web Interaction: Navigate web pages, click buttons, and fill forms automatically—enhancing workflow automation and AI assistant capabilities.

How to set it up

Windsurf

1. Ensure prerequisites such as Node.js are installed.
2. Locate your Windsurf configuration file (e.g., .windsurfrc).
3. Add the Browserbase MCP Server to the mcpServers object:

   {
     "mcpServers": {
       "browserbase": {
         "command": "npx",
         "args": ["@browserbase/mcp-server-browserbase@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the MCP Server appears in your Windsurf UI.

Securing API Keys (Example)

{
  "mcpServers": {
    "browserbase": {
      "env": {
        "BROWSERBASE_API_KEY": "your-api-key"
      },
      "inputs": {
        "projectId": "your-project-id"
      }
    }
  }
}

Claude

1. Install Node.js if required.
2. Locate Claude’s configuration file.
3. Add the following snippet to include Browserbase MCP:

   {
     "mcpServers": {
       "browserbase": {
         "command": "npx",
         "args": ["@browserbase/mcp-server-browserbase@latest"]
       }
     }
   }
   

4. Save changes and restart Claude.
5. Check that Browserbase MCP is listed among available servers.

Cursor

1. Make sure Node.js is installed.
2. Open Cursor’s configuration setup.
3. Add Browserbase MCP using:

   {
     "mcpServers": {
       "browserbase": {
         "command": "npx",
         "args": ["@browserbase/mcp-server-browserbase@latest"]
       }
     }
   }
   

4. Restart Cursor.
5. Confirm the server is operational within Cursor’s UI.

Cline

1. Install Node.js and other dependencies as needed.
2. Edit Cline’s configuration to include Browserbase MCP:

   {
     "mcpServers": {
       "browserbase": {
         "command": "npx",
         "args": ["@browserbase/mcp-server-browserbase@latest"]
       }
     }
   }
   

3. Save the configuration and restart Cline.
4. Validate successful integration.

Note: Always store API keys and sensitive data using environment variables as shown in the Windsurf example above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "browserbase": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “browserbase” to your actual MCP server name and replace the URL with your own MCP server URL.

Overview

Based on the above, the Browserbase MCP Server provides a robust and popular browser automation backend for LLMs, but lacks detailed prompt, resource, and tool documentation in its public readme or code root.

Our opinion

This MCP server is highly popular, actively developed, and covers a valuable AI automation use case. However, the lack of detailed, structured documentation for prompts, tools, and resources limits its immediate accessibility and extensibility for new developers. Overall, it’s a solid, production-grade backbone, but could be improved with more comprehensive docs.

MCP Score