What is Cloudfare Mcp

Cloudfare-MCP is a project that provides a Streamlit-based web interface for interacting with public Cloudflare MCP (Model-Controlled Proxy) servers, enabling users to query documentation, gain internet traffic insights, and perform browser actions powered by CAMEL AI.

Use cases

Use cases include querying Cloudflare’s documentation for technical support, analyzing internet traffic trends, fetching web page content, taking screenshots, and converting web pages to Markdown format.

How to use

To use Cloudfare-MCP, clone the repository, set up a virtual environment, install dependencies, and configure your API key in the ‘mcp_config.json’ file. Then, run the application to access its features through the web interface.

Key features

Key features include an interactive user interface, tabbed navigation for different Cloudflare services (Documentation Server, Radar Server, Browser Server), integration with CAMEL AI for language processing, and easy configuration through a JSON file.

Where to use

Cloudfare-MCP can be used in fields such as web development, data analysis, and digital marketing, where insights from Cloudflare’s services and documentation are valuable.

Cloudflare MCP with Camel-ai

This project provides a Streamlit-based web interface to interact with public Cloudflare MCP (Model-Controlled Proxy) servers. It allows users to easily query Cloudflare’s documentation, get internet traffic insights from Cloudflare Radar, and perform browser actions like fetching web pages or taking screenshots, all powered by CAMEL AI.

Features

• Interactive UI: A user-friendly interface built with Streamlit.
• Tabbed Navigation: Separate tabs for different Cloudflare services:
  • 📚 Documentation Server: Ask questions and get information from Cloudflare’s official documentation.
  • 📊 Radar Server: Access insights on global internet traffic trends, URL analysis, DNS analytics, and HTTP protocol analysis.
  • 🌐 Browser Server: Fetch web page content, take screenshots of URLs, or convert web pages to Markdown.
• Powered by CAMEL AI: Leverages the CAMEL AI library to interact with the MCP servers and process language queries.
• Easy Configuration: Uses a mcp_config.json file to define accessible MCP servers.

Prerequisites

• Python 3.8+
• Node.js and npm (for npx mcp-remote if your mcp_config.json uses it)
• A Gemini API Key (or another compatible model API key supported by CAMEL AI)

Setup

1. Clone the repository (if applicable):

   # git clone <repository-url>
   # cd <repository-name>
   

2. Create and activate a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install dependencies:

   pip install -r requirements.txt
   

   If mcp-remote is used by your mcp_config.json (as in the default configuration for this project), ensure it can be executed. You might need to install it globally or ensure npx can run it:

   npm install -g mcp-remote 
   

4. Set up your API Key:
   Create a .env file in the root directory of the project and add your Gemini API key:

   GEMINI_API_KEY="YOUR_GEMINI_API_KEY_HERE"
   

5. Configure MCP Servers:
   The mcp_config.json file lists the MCP servers the application will connect to. The default configuration includes public Cloudflare servers:

   {
     "servers": [
       {
         "name": "cloudflare_docs",
         "url": "https://docs.mcp.cloudflare.com/sse"
       },
       {
         "name": "cloudflare_radar",
         "url": "https://radar.mcp.cloudflare.com/sse"
       },
       {
         "name": "cloudflare_browser",
         "url": "https://browser.mcp.cloudflare.com/sse"
       }
     ]
   }
   

   If your mcp_config.json uses the mcpServers block with command: "npx" and args: ["mcp-remote", "..."], ensure npx and mcp-remote are correctly installed and accessible in your system’s PATH.

Running the Application

Once the setup is complete, run the Streamlit application using:

streamlit run app.py

The application will open in your default web browser, usually at http://localhost:8501.

Project Structure

• app.py: The main Streamlit application script.
• mcp_config.json: Configuration file for MCP servers.
• requirements.txt: Python dependencies.
• .env: (You create this) Stores your API key.
• README.md: This file.

Troubleshooting

• openai.InternalServerError: Error code: 500: This usually indicates a temporary issue with the backend LLM service (e.g., Gemini). Try again after a few moments. If persistent, check the status of the LLM provider.
• AttributeError: 'ChatAgentResponse' object has no attribute 'content': Ensure your app.py correctly parses the response from agent.step(). It should typically be response.msgs[0].content.
• mcp-remote not found: If your mcp_config.json uses mcp-remote, ensure it’s installed (npm install -g mcp-remote) and npx is in your system’s PATH.

Contributing

Feel free to open issues or submit pull requests if you have suggestions for improvements or bug fixes.