What is Puppeteer Vision Mcp

Puppeteer-vision-mcp is a Model Context Protocol (MCP) server designed for scraping webpages and converting them into markdown format using technologies like Puppeteer, Readability, and Turndown. It incorporates AI-driven capabilities to automatically manage interactive elements such as cookies and captchas.

Use cases

Use cases include scraping news articles for content analysis, extracting data from subscription-based websites, automating the retrieval of information from interactive web applications, and converting complex HTML pages into markdown for easier readability and editing.

How to use

To use puppeteer-vision-mcp, clone the repository, install the necessary dependencies, and build the project. Configure the environment by creating a .env file with your OpenAI API key and other optional settings. Run the server to start scraping webpages.

Key features

Key features include stealth mode scraping with Puppeteer, AI-powered interaction for handling cookies and captchas, extraction of main content using Mozilla’s Readability, conversion of HTML to well-formatted Markdown, and special handling for structured content like code blocks and tables.

Where to use

Puppeteer-vision-mcp can be used in various fields such as web scraping, content aggregation, data analysis, and automated testing, particularly where interaction with dynamic web content is required.

Puppeteer vision MCP Server

This Model Context Protocol (MCP) server provides a tool for scraping webpages and converting them to markdown format using Puppeteer, Readability, and Turndown. It features AI-driven interaction capabilities to handle cookies, captchas, and other interactive elements automatically.

Now easily runnable via npx!

Features

• Scrapes webpages using Puppeteer with stealth mode
• Uses AI-powered interaction to automatically handle:
  • Cookie consent banners
  • CAPTCHAs
  • Newsletter or subscription prompts
  • Paywalls and login walls
  • Age verification prompts
  • Interstitial ads
  • Any other interactive elements blocking content
• Extracts main content with Mozilla’s Readability
• Converts HTML to well-formatted Markdown
• Special handling for code blocks, tables, and other structured content
• Accessible via the Model Context Protocol
• Option to view browser interaction in real-time by disabling headless mode
• Easily consumable as an npx package.

Quick Start with NPX

The recommended way to use this server is via npx, which ensures you’re running the latest version without needing to clone or manually install.

1. Prerequisites: Ensure you have Node.js and npm installed.

2. Environment Setup:
   The server requires an OPENAI_API_KEY. You can provide this and other optional configurations in two ways:

   • .env file: Create a .env file in the directory where you will run the npx command.
   • Shell Environment Variables: Export the variables in your terminal session.

   Example .env file or shell exports:

   # Required
   OPENAI_API_KEY=your_api_key_here
   
   # Optional (defaults shown)
   # VISION_MODEL=gpt-4.1
   # API_BASE_URL=https://api.openai.com/v1   # Uncomment to override
   # TRANSPORT_TYPE=stdio                     # Options: stdio, sse, http
   # USE_SSE=true                             # Deprecated: use TRANSPORT_TYPE=sse instead
   # PORT=3001                                # Only used in sse/http modes
   # DISABLE_HEADLESS=true                    # Uncomment to see the browser in action
   

3. Run the Server:
   Open your terminal and run:

   npx -y puppeteer-vision-mcp-server
   

   • The -y flag automatically confirms any prompts from npx.
   • This command will download (if not already cached) and execute the server.
   • By default, it starts in stdio mode. Set TRANSPORT_TYPE=sse or TRANSPORT_TYPE=http for HTTP server modes.

Using as an MCP Tool with NPX

This server is designed to be integrated as a tool within an MCP-compatible LLM orchestrator. Here’s an example configuration snippet:

When configured this way, the MCP orchestrator will manage the lifecycle of the puppeteer-vision-mcp-server process.

Environment Configuration Details

Regardless of how you run the server (NPX or local development), it uses the following environment variables:

• OPENAI_API_KEY: (Required) Your API key for accessing the vision model.
• VISION_MODEL: (Optional) The model to use for vision analysis.
  • Default: gpt-4.1
  • Can be any model with vision capabilities.
• API_BASE_URL: (Optional) Custom API endpoint URL.
  • Use this to connect to alternative OpenAI-compatible providers (e.g., Together.ai, Groq, Anthropic, local deployments).
• TRANSPORT_TYPE: (Optional) The transport protocol to use.
  • Options: stdio (default), sse, http
  • stdio: Direct process communication (recommended for most use cases)
  • sse: Server-Sent Events over HTTP (legacy mode)
  • http: Streamable HTTP transport with session management
• USE_SSE: (Optional, deprecated) Set to true to enable SSE mode over HTTP.
  • Deprecated: Use TRANSPORT_TYPE=sse instead.
• PORT: (Optional) The port for the HTTP server in SSE or HTTP mode.
  • Default: 3001.
• DISABLE_HEADLESS: (Optional) Set to true to run the browser in visible mode.
  • Default: false (browser runs in headless mode).

Communication Modes

The server supports three communication modes:

1. stdio (Default): Communicates via standard input/output.
   • Perfect for direct integration with LLM tools that manage processes.
   • Ideal for command-line usage and scripting.
   • No HTTP server is started. This is the default mode.
2. SSE mode: Communicates via Server-Sent Events over HTTP.
   • Enable by setting TRANSPORT_TYPE=sse in your environment.
   • Starts an HTTP server on the specified PORT (default: 3001).
   • Use when you need to connect to the tool over a network.
   • Connect to: http://localhost:3001/sse
3. HTTP mode: Communicates via Streamable HTTP transport with session management.
   • Enable by setting TRANSPORT_TYPE=http in your environment.
   • Starts an HTTP server on the specified PORT (default: 3001).
   • Supports full session management and resumable connections.
   • Connect to: http://localhost:3001/mcp

Tool Usage (MCP Invocation)

The server provides a scrape-webpage tool.

Tool Parameters:

• url (string, required): The URL of the webpage to scrape.
• autoInteract (boolean, optional, default: true): Whether to automatically handle interactive elements.
• maxInteractionAttempts (number, optional, default: 3): Maximum number of AI interaction attempts.
• waitForNetworkIdle (boolean, optional, default: true): Whether to wait for network to be idle before processing.

Response Format:

The tool returns its result in a structured format:

• content: An array containing a single text object with the raw markdown of the scraped webpage.
• metadata: Contains additional information:
  • message: Status message.
  • success: Boolean indicating success.
  • contentSize: Size of the content in characters (on success).

Example Success Response:

{
  "content": [
    {
      "type": "text",
      "text": "# Page Title\n\nThis is the content..."
    }
  ],
  "metadata": {
    "message": "Scraping successful",
    "success": true,
    "contentSize": 8734
  }
}

Example Error Response:

{
  "content": [
    {
      "type": "text",
      "text": ""
    }
  ],
  "metadata": {
    "message": "Error scraping webpage: Failed to load the URL",
    "success": false
  }
}

How It Works

AI-Driven Interaction

The system uses vision-capable AI models (configurable via VISION_MODEL and API_BASE_URL) to analyze screenshots of web pages and decide on actions like clicking, typing, or scrolling to bypass overlays and consent forms. This process repeats up to maxInteractionAttempts.

Content Extraction

After interactions, Mozilla’s Readability extracts the main content, which is then sanitized and converted to Markdown using Turndown with custom rules for code blocks and tables.

Installation & Development (for Modifying the Code)

If you wish to contribute, modify the server, or run a local development version:

1. Clone the Repository:

   git clone https://github.com/djannot/puppeteer-vision-mcp.git
   cd puppeteer-vision-mcp
   

2. Install Dependencies:

   npm install
   

3. Build the Project:

   npm run build
   

4. Set Up Environment:
   Create a .env file in the project’s root directory with your OPENAI_API_KEY and any other desired configurations (see “Environment Configuration Details” above).

5. Run for Development:

   npm start # Starts the server using the local build
   

   Or, for automatic rebuilding on changes:

   npm run dev
   

Customization (for Developers)

You can modify the behavior of the scraper by editing:

• src/ai/vision-analyzer.ts (analyzePageWithAI function): Customize the AI prompt.
• src/ai/page-interactions.ts (executeAction function): Add new action types.
• src/scrapers/webpage-scraper.ts (visitWebPage function): Change Puppeteer options.
• src/utils/markdown-formatters.ts: Adjust Turndown rules for Markdown conversion.

Dependencies

Key dependencies include:

• @modelcontextprotocol/sdk
• puppeteer, puppeteer-extra
• @mozilla/readability, jsdom
• turndown, sanitize-html
• openai (or compatible API for vision models)
• express (for SSE mode)
• zod