What features does Cloudinary MCP server provide?

Q: What features does Cloudinary MCP server provide?

Asset Management: Upload, manage, and transform media assets with advanced search, folders, tags, relationships, and the ability to generate archives and download links.. Environment Config: Configure upload presets, transformation settings, streaming profiles, webhook notifications, and upload mappings for asset pipelines.. Structured Metadata: Create and manage structured metadata fields, set conditional rules and validation, and organize/search metadata configurations and relationships.. Analysis: AI-powered content analysis including tagging, moderation, captioning, object detection, quality analysis, and safety checks.. MediaFlows: Build and manage low-code workflow automations, query existing PowerFlow automations, and automate moderation, approvals, and notifications with metadata-driven logic.. Remote MCP Servers: Cloud-hosted MCP endpoints ready to use, accessed via SSE (remote servers like asset-management, environment-config, structured-metadata, analysis).. Local MCP Servers & Docker: Run MCP servers locally via npm packages or Docker images, with flexible authentication and transport options.. Authentication & Debugging: Multiple credential methods (CLOUDINARY_URL or individual env vars) and CLOUDINARY_DEBUG for enhanced payload debugging.

Cloudinary MCP Server 2025: Features & Setup Guide

Overview

Cloudinary's MCP Servers provide a standardized Model Context Protocol interface to manage media workflows through AI agents. This repository exposes a set of MCP servers that make Cloudinary's media management capabilities available as tools for AI applications (e.g., Cursor, Claude) to interact with via natural language. The servers cover core capabilities across the media lifecycle: uploading images and videos, applying transformations, configuring automated processing pipelines, and organizing assets with structured metadata. They also enable AI-powered content analysis, tagging, moderation, and governance tools. The solution supports both remote, Cloudinary-hosted endpoints (SSE-based) and local deployments (npm packages and Docker images), with flexible authentication methods and transport options. Documentation includes configuration examples for remote and local servers, instructions for running via Server-Sent Events (SSE) or stdio, and detailed guidance on porting credentials securely. Features by server describe specific capabilities for Asset Management, Environment Config, Structured Metadata, Analysis, and MediaFlows, including advanced search, presets, streaming profiles, webhook notifications, conditional metadata, and low-code workflow automations. Overall, these MCP servers empower developers to build AI-enabled media workflows with automated processing, analysis, and organizational capabilities.

Features

Asset Management

Upload, manage, and transform media assets with advanced search, folders, tags, relationships, and the ability to generate archives and download links.

Environment Config

Configure upload presets, transformation settings, streaming profiles, webhook notifications, and upload mappings for asset pipelines.

Structured Metadata

Create and manage structured metadata fields, set conditional rules and validation, and organize/search metadata configurations and relationships.

Analysis

AI-powered content analysis including tagging, moderation, captioning, object detection, quality analysis, and safety checks.

MediaFlows

Build and manage low-code workflow automations, query existing PowerFlow automations, and automate moderation, approvals, and notifications with metadata-driven logic.

Remote MCP Servers

Cloud-hosted MCP endpoints ready to use, accessed via SSE (remote servers like asset-management, environment-config, structured-metadata, analysis).

Local MCP Servers & Docker

Run MCP servers locally via npm packages or Docker images, with flexible authentication and transport options.

Authentication & Debugging

Multiple credential methods (CLOUDINARY_URL or individual env vars) and CLOUDINARY_DEBUG for enhanced payload debugging.