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Web Content MCP Server

Web Content MCP Server

by amotivv

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.

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

Related MCP server: MCP Web Tools Server

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

Available Tools

4 tools
extract_structured_contentC

Extracts structured content from a web page using CSS selectors

Inputs: selectors (required), url (required).

fetch_pageC

Fetches and processes a web page for LLM context

Inputs: url (required), includeScreenshot, maxContentLength.

search_documentationC

Searches Cloudflare documentation and returns relevant content

Inputs: query (required), maxResults.

summarize_contentC

Summarizes web content for more concise LLM context

Inputs: url (required), maxLength.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    B3.1/5.0

    Scored across 4 tools

    Disambiguation4/5

    The tools are mostly distinct in purpose, with extract_structured_content, fetch_page, and summarize_content each handling different aspects of web content processing. However, fetch_page and summarize_content could be slightly confused as both relate to processing web content for LLM context, though their specific focuses differ.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case, such as extract_structured_content and fetch_page. This uniformity makes the set predictable and easy to understand, with no deviations in naming conventions.

    Tool Count3/5

    With 4 tools, the count is on the lower side for a web content server, which might feel thin for covering a broad domain like web content processing. However, it is reasonable for a focused set, though it could benefit from additional tools for more comprehensive coverage.

    Completeness2/5

    There are significant gaps in the tool surface for web content processing. For example, there are no tools for updating or deleting content, handling dynamic content like JavaScript, or managing multiple pages. The inclusion of search_documentation is also inconsistent with the general web content focus, creating a notable gap in core operations.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

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