Cloudinary MCP Server

What does “Cloudinary” MCP Server do?

The Cloudinary MCP (Model Context Protocol) Server enables AI assistants and clients to upload images and videos to Cloudinary, a popular cloud-based media management platform. By acting as a bridge between AI tools (such as Claude Desktop) and Cloudinary, this server streamlines the process of handling media assets, allowing assistants to perform actions like uploading, tagging, and organizing files directly through automated workflows. This significantly enhances development productivity by automating media handling tasks, integrating external storage, and enabling seamless API interactions for managing rich media content in various applications.

List of Prompts

No prompt templates are listed in the repository or documentation.

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List of Resources

No explicit resources are documented in the repository or README.

List of Tools

• upload
  Upload images and videos to Cloudinary.
  • Parameters:
    • file (required): Path to file, URL, or base64 data URI to upload
    • resource_type (optional): Type of resource (‘image’, ‘video’, or ‘raw’)
    • public_id (optional): Custom public ID for the uploaded asset
    • overwrite (optional): Whether to overwrite existing assets with the same public ID
    • tags (optional): Array of tags to assign to the uploaded asset

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Use Cases of this MCP Server

• Automated Media Uploads:
  Developers or AI assistants can automatically upload images and videos to Cloudinary from local paths, URLs, or data URIs, streamlining media asset management in projects.

• Media Tagging and Organization:
  Assets can be tagged and assigned custom public IDs upon upload, making it easier to organize, search, and manage large media libraries.

• Content Delivery Optimization:
  By uploading media to Cloudinary, developers can leverage Cloudinary’s CDN and transformation features, improving end-user experience with optimized and fast-loading media.

• Integration with AI Workflows:
  The MCP server allows AI agents (e.g., Claude Desktop) to incorporate media upload steps as part of larger, automated workflows, such as generating content and instantly uploading results.

• Cross-platform Media Handling:
  Supports uploads from various sources (file path, URL, base64), making it versatile for different developer environments and automation scripts.

How to set it up

Windsurf

No specific Windsurf instructions are provided.

Claude

1. Install Node.js (version 18 or higher) from nodejs.org .

2. Locate the Claude configuration directory:

   • Windows: C:\Users\NAME\AppData\Roaming\Claude
   • macOS: ~/Library/Application Support/Claude/

3. Edit your MCP settings file and add:

   {
     "mcpServers": {
       "cloudinary": {
         "command": "npx",
         "args": ["@felores/cloudinary-mcp-server@latest"],
         "env": {
           "CLOUDINARY_CLOUD_NAME": "your_cloud_name",
           "CLOUDINARY_API_KEY": "your_api_key",
           "CLOUDINARY_API_SECRET": "your_api_secret"
         }
       }
     }
   }
   

4. Replace environment variables with your Cloudinary credentials from the Cloudinary Console .

5. Save the file and restart Claude Desktop.

Securing API Keys (Environment Variables)

Example JSON configuration:

{
  "mcpServers": {
    "cloudinary": {
      "command": "npx",
      "args": ["@felores/cloudinary-mcp-server@latest"],
      "env": {
        "CLOUDINARY_CLOUD_NAME": "your_cloud_name",
        "CLOUDINARY_API_KEY": "your_api_key",
        "CLOUDINARY_API_SECRET": "your_api_secret"
      }
    }
  }
}

Cursor

No specific Cursor instructions are provided.

Cline

1. Ensure Node.js is installed.

2. Add the server configuration to your Cline MCP settings file:

   {
     "mcpServers": {
       "cloudinary": {
         "command": "node",
         "args": ["c:/path/to/cloudinary-mcp-server/dist/index.js"],
         "env": {
           "CLOUDINARY_CLOUD_NAME": "your_cloud_name",
           "CLOUDINARY_API_KEY": "your_api_key",
           "CLOUDINARY_API_SECRET": "your_api_secret"
         }
       }
     }
   }
   

3. Install dependencies and build the server:

   npm install
   npm run build
   

4. Save your configuration and restart Cline.

Securing API Keys (Environment Variables)

Example JSON configuration (as above).

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:

{
  "cloudinary": {
    "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 “cloudinary” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

• Roots support: Not mentioned (assume ⛔).

Based on the tables, the Cloudinary MCP Server is straightforward and focused, with clear instructions and a useful tool, but it lacks resource and prompt template definitions and does not mention Roots or Sampling support. For a single-purpose MCP server, it does its job well but does not offer the full breadth of MCP features. Score: 6/10

MCP Score