Universal Image MCP

Universal Image MCP - Multi-Provider AI Image Generation Server for Claude Desktop & MCP Clients

Universal MCP server for AI image generationsupporting AWS Bedrock (Nova Canvas), OpenAI (GPT Image, DALL-E), and Google Gemini (Imagen 4). Generate, transform, and edit images using multiple AI models through a single Model Context Protocol interface.

Universal Image MCP is aModel Context Protocol (MCP) serverthat provides unified access to multiple AI image generation providers. Whether you're usingClaude Desktop,Kiro IDE, or any MCP-compatible client, this server lets you generate and transform images using: n

- AWS Bedrock- Amazon Nova Canvas for enterprise-grade image generation
- OpenAI- GPT Image 1.5, ChatGPT Image, DALL-E models
- Google Gemini- Gemini 2.5 Flash Image, Imagen 4, Imagen 4 Ultra

Perfect for developers building AI applications, content creators, and anyone needing programmatic access to multiple image generation APIs through a single interface.

Comparison of architecture diagrams generated by different models using this MCP server:

Technical architecture diagram of a Universal Image MCP Server system. The diagram shows: Top layer: MCP Client (Claude Desktop, Kiro IDE) connecting via Model Context Protocol Middle layer: Universal Image MCP Server (FastMCP) with three main components: 1. Server Module (server.py) - handles list_models, generate_image, transform_image, prompt_guide tools 2. Provider Module (providers.py) - manages lazy initialization and provider abstraction 3. Configuration - environment variables for ENABLE_AWS, ENABLE_OPENAI, ENABLE_GEMINI Bottom layer: Three provider boxes side by side: - AWS Bedrock (boto3) - Amazon Nova Canvas, with AWS credentials and region config - OpenAI API - GPT Image 1.5, ChatGPT Image Latest, with API key - Google Gemini API - Gemini 2.5 Flash, Imagen 4, with API key Data flow arrows showing: - Client sends tool requests to Server - Server routes to appropriate Provider based on model_id - Providers make API calls to their respective services - Image data flows back through the chain Clean, professional software architecture diagram style with boxes, arrows, and labels. Use blue and gray color scheme. Modern technical documentation aesthetic. Isometric or layered view showing clear separation of concerns.

Same technical architecture content as above, but rendered in: 3D clay art style with soft rounded shapes, pastel colors, cute minimalist aesthetic, soft studio lighting, clean composition with depth and shadows.

Note: 3D Clay Art versions useds3tablearch.pngas a reference image for style guidance.

- 🔄Multi-Provider Support- Switch between AWS Bedrock, OpenAI, and Google Gemini seamlessly
- 🚀Dynamic Model Discovery- Automatically fetches latest available models from each provider API
- ⚡Lazy Initialization- Provider clients load only when needed for optimal performance
- 🎨Reference Image Support- Generate new images based on existing image styles
- 📐Configurable Dimensions- Custom width/height for supported AI models
- 📚Built-in Prompt Guide- Best practices for writing effective image generation prompts
- 🔌MCP Protocol- Works with Claude Desktop, Kiro IDE, and all MCP-compatible clients
- 🐍Python 3.11+- Modern Python with type hints and async support

Or use with uvx (recommended for MCP servers):

Add to your Claude Desktop MCP configuration file:

macOS:~/Library/Application Support/Claude/claude_desktop_config.json
Windows:%APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "universal-image-mcp": { "command": "uvx", "args": ["universal-image-mcp@latest"], "env": { "ENABLE_AWS": "true", "AWS_PROFILE": "default", "AWS_REGION": "us-east-1", "ENABLE_OPENAI": "true", "OPENAI_API_KEY": "sk-...", "ENABLE_GEMINI": "true", "GEMINI_API_KEY": "..." } } } }

{ "mcpServers": { "universal-image-mcp": { "command": "uvx", "args": ["universal-image-mcp@latest"], "env": { "ENABLE_AWS": "true", "ENABLE_OPENAI": "true", "OPENAI_API_KEY": "sk-...", "ENABLE_GEMINI": "true", "GEMINI_API_KEY": "..." } } } }

Before using this MCP server, you'll need to obtain credentials for the providers you want to use.

AWS Bedrock uses your local AWS credentials. You have several options:

- Install theAWS CLI
- Runaws configureand provide your access key, secret key, and region
- Official guide:AWS CLI Configuration

- Create~/.aws/credentialswith your access keys
- Create~/.aws/configwith your region settings
- Official guide:Shared Config and Credentials Files

- SetAWS_ACCESS_KEY_ID,AWS_SECRET_ACCESS_KEY, andAWS_REGION
- Official guide:AWS CLI Authentication

- VisitOpenAI Platform
- Sign up or log in to your account

- Go toAPI Keys page
- Click "Create new secret key"
- Give it a descriptive name (optional)
- Copy the key immediately (you won't be able to see it again)

- OpenAI requires payment information to use the API
- Navigate toBillingto add payment details

Official Documentation:OpenAI Quickstart Guide

- For production use, you can useVertex AI on Google Cloud
- This provides more enterprise features and billing controls

Official Documentation:Gemini API Quickstart

- Model Context Protocol- Official MCP documentation
- Claude Desktop- AI assistant with MCP support
- FastMCP- Python framework for building MCP servers

Hosted MCP server for AudioPod's audio AI: text-to-speech, voice cloning, music generation, stem and speaker separation, transcription, denoise, and voice conversion.