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Cloudflare Browser Rendering & MCP Server: AI-Powered Content for LLMs - MCP Implementation

Cloudflare Browser Rendering & MCP Server: AI-Powered Content for LLMs

Power LLM context delivery with Cloudflare Browser Rendering experiments (REST/Workers APIs) and an MCP server for seamless web content extraction and integration.

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About Cloudflare Browser Rendering & MCP Server

What is Cloudflare Browser Rendering & MCP Server: AI-Powered Content for LLMs?

Cloudflare Browser Rendering is a service that allows dynamic web content scraping and preprocessing, while the MCP (Machine Content Processor) Server acts as an intermediary to format this data for Large Language Models (LLMs). Together, they enable developers to extract, sanitize, and structure web content efficiently for AI applications like chatbots or data analysis platforms.

How to Use Cloudflare Browser Rendering & MCP Server: AI-Powered Content for LLMs?

  1. Setup Environment: Install dependencies via npm and configure the Cloudflare Worker with the nodejs_compat flag.
  2. Deploy Worker: Use the puppeteer binding pattern to launch browsers and process pages.
  3. Run Experiments: Test content extraction workflows using provided debugging tools and test suites.
  4. Launch MCP Server: Configure environment variables and start the server to access APIs like fetch_page and search_documentation.

Cloudflare Browser Rendering & MCP Server Features

Key Features of Cloudflare Browser Rendering & MCP Server: AI-Powered Content for LLMs?

  • Dynamic Content Handling: Renders JavaScript-heavy web pages for accurate content capture.
  • API Flexibility: Supports both REST endpoints and direct Worker interactions via the @cloudflare/puppeteer package.
  • Content Processing Tools: Built-in sanitization, metadata extraction, and format conversion for LLM compatibility.
  • Configurable Pipelines: Customizable workflows through environment variables and MCP server settings.

Use Cases of Cloudflare Browser Rendering & MCP Server: AI-Powered Content for LLMs?

Developers use this stack for:

  • Powering chatbots with real-time web data
  • Curating training datasets for machine learning models
  • Automating compliance checks on dynamically generated content
  • Building educational tools for web scraping best practices

Cloudflare Browser Rendering & MCP Server FAQ

FAQ from Cloudflare Browser Rendering & MCP Server: AI-Powered Content for LLMs?

Why is the nodejs_compat flag required?

Enables compatibility with Node.js modules and ensures proper execution of Puppeteer in Cloudflare Workers.

How to avoid resource leaks?

Always close browser instances after use with browser.close() to prevent memory exhaustion.

Where should environment variables be set?

Defined in both the Worker dashboard and local development environments, including sensitive credentials.

Can I integrate with existing LLM APIs?

Yes - MCP Server outputs are designed to interface seamlessly with OpenAI, Anthropic, and other model APIs.

Content

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();
  1. When deploying a Worker that uses the Browser Rendering binding, you need to enable the nodejs_compat compatibility flag.
  2. Always close the browser after use to avoid resource leaks.

License

MIT

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