Cloudflare MCP Server Integration

What does “Cloudflare” MCP Server do?

The Cloudflare MCP (Model Context Protocol) Server acts as a bridge between AI assistants and Cloudflare’s powerful suite of cloud services. By integrating with the Cloudflare MCP Server, AI agents can access, query, and manage configurations, logs, builds, and documentation for Cloudflare accounts using natural language. This server enables developers to automate workflows such as reading account settings, retrieving observability data, making infrastructure changes, and referencing up-to-date Cloudflare documentation. It streamlines development, debugging, and deployment by bringing Cloudflare’s APIs and data directly into AI-powered tools, enhancing productivity, and simplifying cloud management tasks.

List of Prompts

No information about prompt templates is available in the repository.

List of Resources

• Documentation server
  Offers up-to-date reference information on Cloudflare, making it easier for clients to surface relevant context for LLM interactions.
  https://docs.mcp.cloudflare.com/sse

• Workers Bindings server
  Provides access to primitives for building Workers applications, including storage, AI, and compute resources.
  https://bindings.mcp.cloudflare.com/sse

• Workers Builds server
  Delivers insights into and management of Cloudflare Workers builds, facilitating better build management and automation.
  https://builds.mcp.cloudflare.com/sse

• Observability server
  Exposes logs and analytics for debugging and gaining insights into application performance on Cloudflare.
  https://observability.mcp.cloudflare.com/sse

List of Tools

No explicit tool list or server.py with tool definitions is provided in the visible files or documentation.

Use Cases of this MCP Server

• Reference Cloudflare Documentation
  AI assistants can instantly access and surface Cloudflare docs to answer questions, troubleshoot, or provide setup guidance.

• Automate Workers Deployment and Management
  Integrate with Workers Bindings and Builds to automate deployment, configuration, and CI/CD operations through natural language.

• Monitor and Debug Applications
  Use the Observability server to fetch logs and analytics, enabling rapid debugging and performance monitoring directly via AI tools.

• Manage Cloudflare Account Settings
  Query and modify account-level configurations, making it easy to automate repetitive or complex administrative tasks.

• Integrate Cloudflare Insights into Dev Workflows
  Bring build, deployment, and observability data into developer workflows, enhancing visibility and enabling smarter automation.

How to set it up

Windsurf

1. Ensure you have Node.js and a compatible MCP Client installed.
2. Open the Windsurf configuration file (e.g., windsurf.config.json).
3. Add the Cloudflare MCP Server to the mcpServers section:

   {
     "mcpServers": {
       "cloudflare-mcp": {
         "command": "npx",
         "args": ["@cloudflare/mcp-server-cloudflare@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify that the server is listed in the UI.

Claude

1. Install the latest Claude MCP Client.
2. Locate the Claude configuration file.
3. Insert the Cloudflare MCP server:

   {
     "mcpServers": {
       "cloudflare-mcp": {
         "command": "npx",
         "args": ["@cloudflare/mcp-server-cloudflare@latest"]
       }
     }
   }
   

4. Save and restart the Claude client.
5. Confirm the server connection in Claude.

Cursor

1. Ensure Cursor is up to date and supports MCP servers.
2. Edit the Cursor MCP configuration.
3. Add:

   {
     "mcpServers": {
       "cloudflare-mcp": {
         "command": "npx",
         "args": ["@cloudflare/mcp-server-cloudflare@latest"]
       }
     }
   }
   

4. Restart Cursor.
5. Check for Cloudflare MCP availability in Cursor’s tools.

Cline

1. Install prerequisites (e.g., Node.js).
2. Locate the Cline configuration file.
3. Add the server:

   {
     "mcpServers": {
       "cloudflare-mcp": {
         "command": "npx",
         "args": ["@cloudflare/mcp-server-cloudflare@latest"]
       }
     }
   }
   

4. Save and restart Cline.
5. Validate Cloudflare MCP integration.

Securing API Keys
Store sensitive API keys in environment variables. Example JSON configuration:

{
  "mcpServers": {
    "cloudflare-mcp": {
      "command": "npx",
      "args": ["@cloudflare/mcp-server-cloudflare@latest"],
      "env": {
        "CLOUDFLARE_API_TOKEN": "${CLOUDFLARE_API_TOKEN}"
      },
      "inputs": {
        "apiToken": "${CLOUDFLARE_API_TOKEN}"
      }
    }
  }
}

Never hard-code credentials. Use environment variables for security.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "cloudflare-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “cloudflare-mcp” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the above tables, the Cloudflare MCP Server provides excellent documentation, clear resource endpoints, and robust integration instructions, but lacks explicit information on prompt templates and tool definitions, and does not mention sampling or roots support. Its resource coverage and practical integration make it a strong MCP server, but the lack of prompt and tool details prevents a perfect score.

MCP Score

Overall, I would rate the Cloudflare MCP Server as a 7/10. It excels in core documentation, resource exposure, and ease of setup, but would benefit from more explicit prompt and tool listings for maximum MCP client utility.