Cloudinary
Interact with Cloudinary's media management platform using natural language.
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Overview
Model Context Protocol (MCP) is a new, standardized protocol for managing context between large language models (LLMs) and external systems. This repository provides comprehensive MCP servers for Cloudinary's media management platform, enabling you to use natural language to upload, transform, analyze, and organize your media assets directly from AI applications like Cursor and Claude. With these MCP servers, you can seamlessly manage your entire media workflow through conversational AI - from uploading and transforming images and videos, to configuring automated processing pipelines, analyzing content with AI-powered tools, and organizing assets with structured metadata. Whether you're building media-rich applications, managing large asset libraries, or automating content workflows, these servers provide direct access to Cloudinary's full suite of media optimization and management capabilities.
Installation
Docker images offer several benefits:
Configuration
{
"mcpServers": {
"cloudinary-asset-mgmt-remote": {
"url": "https://asset-management.mcp.cloudinary.com/mcp"
},
"cloudinary-env-config-remote": {
"url": "https://environment-config.mcp.cloudinary.com/mcp"
},
"cloudinary-smd-remote": {
"url": "https://structured-metadata.mcp.cloudinary.com/mcp"
},
"cloudinary-analysis-remote": {
"url": "https://analysis.mcp.cloudinary.com/sse"
},
"mediaflows": {
"url": "https://mediaflows.mcp.cloudinary.com/v2/mcp"
}
}
}Capabilities
- Upload and manage media assets
Ask your assistant to upload images, videos, or raw files and organize them with folders, tags, and relationships via the Asset Management server.
- Transform and generate assets
Request on-the-fly image and video transformations, or generate archives and download links for selected media.
- Configure environment settings
Use the Environment Config server to set up upload presets, transformation defaults, streaming profiles, and webhook notifications.
- Create structured metadata fields
Define custom metadata fields with conditional rules and validation to improve asset searchability and organization.
- Run AI-powered content analysis
Leverage the Analysis server for auto-tagging, moderation, captioning, object detection, and image quality assessments.
- Build workflow automations
Use MediaFlows to create and manage low-code automation pipelines with natural language, including conditional logic and approval workflows.
Tags
- media
- cloud-storage
- content-creation
- api
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