by felores
Cloudinary MCP Server is a Model Context Protocol (MCP) server designed to facilitate the uploading of images and videos to Cloudinary. It integrates with Claude Desktop and other compatible MCP clients, providing a streamlined way to manage media assets on the Cloudinary platform.
Cloudinary MCP Server is a Model Context Protocol (MCP) server designed to facilitate the uploading of images and videos to Cloudinary. It integrates with Claude Desktop and other compatible MCP clients, providing a streamlined way to manage media assets on the Cloudinary platform.
To use Cloudinary MCP Server, you need Node.js (version 18 or higher) and npm. The recommended installation method is via npx
, which involves adding a configuration to your Claude settings file with your Cloudinary credentials (Cloud Name, API Key, API Secret). Alternatively, for development or modification, you can clone the repository, install dependencies, and build the project. Once set up, you can utilize the upload
tool within Claude/Cline to upload files by providing the file path, resource type, and optional parameters like public_id
, overwrite
, and tags
.
Q: What are the requirements for running Cloudinary MCP Server? A: You need Node.js (version 18 or higher) and npm installed on your system.
Q: How do I get my Cloudinary credentials? A: You can obtain your Cloud Name, API Key, and API Secret from the Cloudinary Console under API Keys.
Q: Can I upload both images and videos?
A: Yes, the server supports uploading both images and videos, and you can specify the resource_type
during the upload process.
Q: Is it possible to set a custom public ID for uploaded assets?
A: Yes, you can use the public_id
parameter to specify a custom public ID for your uploaded assets.
Q: How do I contribute to the development of Cloudinary MCP Server? A: You can clone the repository, install dependencies, and build the project to start contributing or modifying the server.
This MCP server provides tools for uploading images and videos to Cloudinary through Claude Desktop and compatible MCP clients.
node --version
npm --version
Navigate to the Claude configuration directory:
C:\Users\NAME\AppData\Roaming\Claude
~/Library/Application Support/Claude/
You can also find these directories inside the Claude Desktop app: Claude Desktop > Settings > Developer > Edit Config
Add the following configuration to your MCP settings file:
{
"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"
}
}
}
}
If you want to modify the server or contribute to development:
git clone https://github.com/felores/cloudinary-mcp-server.git
cd cloudinary-mcp-server
npm install
npm run build
First, ensure you have a Cloudinary account and get your credentials from the Cloudinary Console:
Add the server configuration to your Claude/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"
}
}
}
}
For Claude desktop app, edit the configuration file at the appropriate location for your OS.
npm install
npm run build
Upload images and videos to Cloudinary.
Parameters:
file
(required): Path to file, URL, or base64 data URI to uploadresource_type
(optional): Type of resource ('image', 'video', or 'raw')public_id
(optional): Custom public ID for the uploaded assetoverwrite
(optional): Whether to overwrite existing assets with the same public IDtags
(optional): Array of tags to assign to the uploaded assetExample usage in Claude/Cline:
use_mcp_tool({
server_name: "cloudinary",
tool_name: "upload",
arguments: {
file: "path/to/image.jpg",
resource_type: "image",
public_id: "my-custom-id"
}
});
Please log in to share your review and rating for this MCP.
Discover more MCP servers with similar functionality and use cases
by daytonaio
Provides a secure, elastic sandbox environment for executing AI‑generated code with isolated runtimes and sub‑90 ms provisioning.
by awslabs
Specialized servers that expose AWS capabilities through the Model Context Protocol, allowing AI assistants and other applications to retrieve up‑to‑date AWS documentation, manage infrastructure, query services, and perform workflow automation directly from their context.
by awslabs
AWS MCP Servers allow AI agents to interact with and manage a wide range of AWS services using natural language commands. They enable AI-powered cloud management, automated DevOps, and data-driven insights within the AWS ecosystem.
by cloudflare
Remote Model Context Protocol endpoints that let AI clients read, process, and act on data across Cloudflare services such as Workers, Radar, Observability, and more.
by supabase-community
Enables AI assistants to interact directly with Supabase projects, allowing them to query databases, fetch configuration, manage tables, and perform other project‑level operations.
by Azure
azure-mcp is a server that implements the Model Context Protocol (MCP) to connect AI agents with Azure services. It allows developers to interact with Azure resources like Storage, Cosmos DB, and the Azure CLI using natural language commands within their development environment.
by Flux159
MCP Server for Kubernetes management commands, enabling interaction with Kubernetes clusters to manage pods, deployments, and services.
by strowk
Provides a Golang‑based server that enables interaction with Kubernetes clusters via prompts, allowing listing of contexts, namespaces, resources, nodes, pods, events, logs, and executing commands inside pods.
by jamsocket
Run arbitrary Python code securely in persistent, stateful sandboxes that remain available indefinitely.