Browserbase MCP Server

What does “Browserbase” MCP Server do?

The Browserbase MCP Server provides powerful cloud browser automation capabilities by leveraging Browserbase and Stagehand. It enables AI language models to interact with, control, and automate web browsers in a secure cloud environment. With this server, LLMs can perform actions such as navigating web pages, extracting structured data, taking screenshots, monitoring console logs, and executing JavaScript—all programmatically. This extends the abilities of AI assistants to handle web-based workflows, automate repetitive browser tasks, and extract or interact with online content, significantly enhancing developer productivity, testing, and research workflows. The server supports modular integration and is designed to be flexible, supporting multiple LLMs and models.

List of Prompts

No information about prompt templates is provided in the repository.

List of Resources

No explicit resources are detailed in the provided documentation or repository files.

List of Tools

The following capabilities are described as features, which likely correspond to tools exposed by the Browserbase MCP Server:

• Browser Automation: Enables control and orchestration of cloud browsers for navigation and interaction.
• Data Extraction: Allows extraction of structured data from any webpage.
• Console Monitoring: Tracks and analyzes browser console logs for debugging or monitoring.
• Screenshots: Captures full-page or element-specific screenshots.
• Web Interaction: Performs actions such as navigating, clicking, and filling forms on web pages.
• Atomic Instructions (Stagehand MCP): Executes precise, high-level commands like clicks or targeted data extraction.
• Vision Support (Stagehand MCP): Uses annotated screenshots for working with complex DOMs.

Use Cases of this MCP Server

• Web Automation & Testing
  Automate browser tasks for end-to-end testing of web applications, reducing manual QA efforts and increasing reliability.
• Data Extraction & Web Scraping
  Extract structured data, tables, or content from online sources directly into AI workflows for research, analysis, or reporting.
• UI/UX Research
  Capture screenshots and monitor console logs to document and analyze user journeys or application performance.
• AI-Powered Browsing Agents
  Enable LLMs to act as autonomous browsing agents, navigating sites, filling out forms, and interacting with web applications on behalf of users.
• Multi-Model Support & Integration
  Integrate various LLMs (e.g., GPT-4, Claude-3.7 Sonnet) for flexible, model-agnostic browser automation and interaction.

How to set it up

Windsurf

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

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

4. Save the configuration file and restart Windsurf.
5. Verify the setup by checking that the server is running and accessible.

Claude

1. Ensure you have the required runtime (e.g., Node.js).
2. Open the Claude configuration file.
3. Add the Browserbase MCP Server entry:

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

4. Save changes and restart Claude.
5. Confirm connectivity to the MCP server.

Cursor

1. Confirm prerequisites such as Node.js are installed.
2. Edit the Cursor configuration file.
3. Insert the following JSON snippet under mcpServers:

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

4. Save and restart Cursor.
5. Check the logs or status page for confirmation.

Cline

1. Make sure Node.js and other dependencies are present.
2. Find and open the Cline configuration file.
3. Add the Browserbase MCP Server:

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

4. Save the file and restart Cline.
5. Ensure the MCP server initializes without errors.

Securing API Keys with Environment Variables: For any sensitive configuration (such as API keys), use environment variables. For example:

{
  "mcpServers": {
    "browserbase": {
      "command": "npx",
      "args": ["@browserbase/mcp-server-browserbase@latest"],
      "env": {
        "BROWSERBASE_API_KEY": "${BROWSERBASE_API_KEY}"
      },
      "inputs": {
        "apiKey": "${BROWSERBASE_API_KEY}"
      }
    }
  }
}

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 whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above, the Browserbase MCP Server offers strong browser automation features and integration guidance, but lacks explicit prompt and resource documentation. Its open-source license and active repository are positives.

Our opinion

This MCP server is robust in browser automation and integration, but documentation around prompts and resource primitives is lacking. Still, it is well-maintained and supported by an active community. Score: 7/10

MCP Score