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cloudflare-browser-rendering

MCP.Pizza Chef: amotivv

The cloudflare-browser-rendering MCP server leverages Cloudflare's Browser Rendering capabilities to extract and provide web content as structured context for large language models (LLMs). It supports both REST API and Workers Binding API integrations, enabling real-time web content retrieval and rendering. This server is ideal for feeding dynamic, browser-rendered web data into LLM workflows, enhancing their understanding of web environments and enabling richer, context-aware AI interactions.

Use This MCP server To

Extract dynamic web page content for LLM context Render JavaScript-heavy websites for accurate data extraction Provide real-time web content snapshots to LLMs Integrate with Cloudflare Workers for scalable web scraping Test and debug web content extraction workflows Use REST API to fetch rendered web pages Supply browser-rendered HTML to AI agents Enable LLMs to access up-to-date web information

README

Cloudflare Browser Rendering Experiments & MCP Server

This project demonstrates how to use Cloudflare Browser Rendering to extract web content for LLM context. It includes experiments with the REST API and Workers Binding API, as well as an MCP server implementation that can be used to provide web context to LLMs.

Web Content Server MCP server

Project Structure

cloudflare-browser-rendering/
├── examples/                   # Example implementations and utilities
│   ├── basic-worker-example.js # Basic Worker with Browser Rendering
│   ├── minimal-worker-example.js # Minimal implementation
│   ├── debugging-tools/        # Tools for debugging
│   │   └── debug-test.js       # Debug test utility
│   └── testing/                # Testing utilities
│       └── content-test.js     # Content testing utility
├── experiments/                # Educational experiments
│   ├── basic-rest-api/         # REST API tests
│   ├── puppeteer-binding/      # Workers Binding API tests
│   └── content-extraction/     # Content processing tests
├── src/                        # MCP server source code
│   ├── index.ts                # Main entry point
│   ├── server.ts               # MCP server implementation
│   ├── browser-client.ts       # Browser Rendering client
│   └── content-processor.ts    # Content processing utilities
├── puppeteer-worker.js         # Cloudflare Worker with Browser Rendering binding
├── test-puppeteer.js           # Tests for the main implementation
├── wrangler.toml               # Wrangler configuration for the Worker
├── cline_mcp_settings.json.example # Example MCP settings for Cline
├── .gitignore                  # Git ignore file
└── LICENSE                     # MIT License

Prerequisites

  • Node.js (v16 or later)
  • A Cloudflare account with Browser Rendering enabled
  • TypeScript
  • Wrangler CLI (for deploying the Worker)

Installation

  1. Clone the repository:
git clone https://github.com/yourusername/cloudflare-browser-rendering.git
cd cloudflare-browser-rendering
  1. Install dependencies:
npm install

Cloudflare Worker Setup

  1. Install the Cloudflare Puppeteer package:
npm install @cloudflare/puppeteer
  1. Configure Wrangler:
# wrangler.toml
name = "browser-rendering-api"
main = "puppeteer-worker.js"
compatibility_date = "2023-10-30"
compatibility_flags = ["nodejs_compat"]

[browser]
binding = "browser"
  1. Deploy the Worker:
npx wrangler deploy
  1. Test the Worker:
node test-puppeteer.js

Running the Experiments

Basic REST API Experiment

This experiment demonstrates how to use the Cloudflare Browser Rendering REST API to fetch and process web content:

npm run experiment:rest

Puppeteer Binding API Experiment

This experiment demonstrates how to use the Cloudflare Browser Rendering Workers Binding API with Puppeteer for more advanced browser automation:

npm run experiment:puppeteer

Content Extraction Experiment

This experiment demonstrates how to extract and process web content specifically for use as context in LLMs:

npm run experiment:content

MCP Server

The MCP server provides tools for fetching and processing web content using Cloudflare Browser Rendering for use as context in LLMs.

Building the MCP Server

npm run build

Running the MCP Server

npm start

Or, for development:

npm run dev

MCP Server Tools

The MCP server provides the following tools:

  1. fetch_page - Fetches and processes a web page for LLM context
  2. search_documentation - Searches Cloudflare documentation and returns relevant content
  3. extract_structured_content - Extracts structured content from a web page using CSS selectors
  4. summarize_content - Summarizes web content for more concise LLM context

Configuration

To use your Cloudflare Browser Rendering endpoint, set the BROWSER_RENDERING_API environment variable:

export BROWSER_RENDERING_API=https://YOUR_WORKER_URL_HERE

Replace YOUR_WORKER_URL_HERE with the URL of your deployed Cloudflare Worker. You'll need to replace this placeholder in several files:

  1. In test files: test-puppeteer.js, examples/debugging-tools/debug-test.js, examples/testing/content-test.js
  2. In the MCP server configuration: cline_mcp_settings.json.example
  3. In the browser client: src/browser-client.ts (as a fallback if the environment variable is not set)

Integrating with Cline

To integrate the MCP server with Cline, copy the cline_mcp_settings.json.example file to the appropriate location:

cp cline_mcp_settings.json.example ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

Or add the configuration to your existing cline_mcp_settings.json file.

Key Learnings

  1. Cloudflare Browser Rendering requires the @cloudflare/puppeteer package to interact with the browser binding.
  2. The correct pattern for using the browser binding is:
    import puppeteer from '@cloudflare/puppeteer';
    
    // Then in your handler:
    const browser = await puppeteer.launch(env.browser);
    const page = await browser.newPage();
  3. When deploying a Worker that uses the Browser Rendering binding, you need to enable the nodejs_compat compatibility flag.
  4. Always close the browser after use to avoid resource leaks.

License

MIT

cloudflare-browser-rendering FAQ

How does the Cloudflare Browser Rendering MCP server work?
It uses Cloudflare's Browser Rendering APIs to fetch and render web pages, providing the rendered content as structured context to LLMs like OpenAI, Claude, and Gemini.
What APIs does this MCP server support?
It supports both the Cloudflare REST API and the Workers Binding API for flexible integration.
Can this server handle JavaScript-heavy websites?
Yes, it renders pages in a real browser environment, enabling extraction of dynamic content.
How do I deploy this MCP server?
You can deploy it on Cloudflare Workers with the provided example implementations and utilities.
Is this MCP server suitable for real-time web content extraction?
Yes, it is designed to provide up-to-date, browser-rendered web content in real time.
What debugging tools are included?
The project includes debugging utilities and test scripts to verify content extraction and rendering.
Can this MCP server be used with multiple LLM providers?
Yes, it is provider-agnostic and works with OpenAI, Claude, Gemini, and others.
Does it support scalable web scraping?
Yes, leveraging Cloudflare Workers allows scalable and efficient web content extraction.