About Puppeteer Vision Mcp Server
Puppeteer Vision MCP Server scrapes web pages with Puppeteer, extracts main content with Readability and converts it to Markdown with Turndown. It uses a vision-capable model to analyze screenshots and dismiss overlays such as cookie banners and captchas.
Key features
- 01Puppeteer scraping with stealth mode
- 02Vision model driven handling of page overlays
- 03Main content extraction with Readability
- 04HTML to Markdown conversion with code and table rules
- 05Supports stdio, SSE and HTTP transports
- 06Runnable via npx
Use cases
- 01Converting pages behind cookie banners or login walls into Markdown
- 02Supplying web page text to an LLM as context
- 03Watching browser interaction in visible mode while debugging scrapes
MCP support
The server exposes a scrape-webpage tool. MCP clients pass a URL and interaction options and receive the page as Markdown along with status metadata.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEPuppeteer 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
npxpackage.
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.
-
Prerequisites: Ensure you have Node.js and npm installed.
-
Environment Setup: The server requires an
OPENAI_API_KEY. You can provide this and other optional configurations in two ways:.envfile: Create a.envfile in the directory where you will run thenpxcommand.- Shell Environment Variables: Export the variables in your terminal session.
Example
.envfile 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 -
Run the Server: Open your terminal and run:
npx -y puppeteer-vision-mcp-server- The
-yflag automatically confirms any prompts fromnpx. - This command will download (if not already cached) and execute the server.
- By default, it starts in
stdiomode. SetTRANSPORT_TYPE=sseorTRANSPORT_TYPE=httpfor HTTP server modes.
- The
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:
{
"mcpServers": {
"web-scraper": {
"command": "npx",
"args": ["-y", "puppeteer-vision-mcp-server"],
"env": {
"OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE",
// Optional:
// "VISION_MODEL": "gpt-4.1",
// "API_BASE_URL": "https://api.example.com/v1",
// "TRANSPORT_TYPE": "stdio", // or "sse" or "http"
// "DISABLE_HEADLESS": "true" // To see the browser during operations
}
}
// ... other MCP servers
}
}
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.
- Default:
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
- Options:
USE_SSE: (Optional, deprecated) Set totrueto enable SSE mode over HTTP.- Deprecated: Use
TRANSPORT_TYPE=sseinstead.
- Deprecated: Use
PORT: (Optional) The port for the HTTP server in SSE or HTTP mode.- Default:
3001.
- Default:
DISABLE_HEADLESS: (Optional) Set totrueto run the browser in visible mode.- Default:
false(browser runs in headless mode).
- Default:
Communication Modes
The server supports three communication modes:
- 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.
- SSE mode: Communicates via Server-Sent Events over HTTP.
- Enable by setting
TRANSPORT_TYPE=ssein 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
- Enable by setting
- HTTP mode: Communicates via Streamable HTTP transport with session management.
- Enable by setting
TRANSPORT_TYPE=httpin 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
- Enable by setting
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:
-
Clone the Repository:
git clone https://github.com/djannot/puppeteer-vision-mcp.git cd puppeteer-vision-mcp -
Install Dependencies:
npm install -
Build the Project:
npm run build -
Set Up Environment: Create a
.envfile in the project's root directory with yourOPENAI_API_KEYand any other desired configurations (see "Environment Configuration Details" above). -
Run for Development:
npm start # Starts the server using the local buildOr, 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(analyzePageWithAIfunction): Customize the AI prompt.src/ai/page-interactions.ts(executeActionfunction): Add new action types.src/scrapers/webpage-scraper.ts(visitWebPagefunction): Change Puppeteer options.src/utils/markdown-formatters.ts: Adjust Turndown rules for Markdown conversion.
Dependencies
Key dependencies include:
@modelcontextprotocol/sdkpuppeteer,puppeteer-extra@mozilla/readability,jsdomturndown,sanitize-htmlopenai(or compatible API for vision models)express(for SSE mode)zod
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
