MCP Image Generator
An MCP server for generating images using Together AI or Replicate models.
MCP Image Generator
A Model Context Protocol (MCP) server for generating images using Together AI's image generation models. This MCP Server can be run locally or using an SSE endpoint.
The MCP Image Generator required a provider, only "Replicate" and "Together" are supported currently. You need to set the TOGETHER_API_KEY or REPLICATE_API_TOKEN environment variables. and set the PROVIDER environment variable to "replicate" or "together"/
SSE Endpoint (Docker environment)
Clone the repository
git clone https://github.com/gmkr/mcp-imagegen.git
cd mcp-imagegen
Build and run Docker container
docker build -f Dockerfile.server -t mcp-imagegen .
docker run -p 3000:3000 mcp-imagegen
Configuring with MCP Client
{
"mcpServers": {
"imagegenerator": {
"url": "http://localhost:3000/sse",
"env": {
"PROVIDER": "replicate",
"REPLICATE_API_TOKEN": "your-replicate-api-token"
}
}
}
}
Adjust the url to the endpoint of the MCP server you want to use. provider can be "replicate" or "together".
Running locally using stdio
Prerequisites
- Node.js
- Together AI API key or Replicate API token
Installation
-
Clone the repository:
git clone https://github.com/gmkr/mcp-imagegen.git cd mcp-imagegen -
Install dependencies:
pnpm install
Configuration
Create a configuration file for your MCP client. Here's an example configuration:
{
"mcpServers": {
"imagegenerator": {
"command": "pnpx",
"args": [
"-y",
"tsx",
"/path/to/mcp-imagegen/src/index.ts"
],
"env": {
"PROVIDER": "replicate",
"REPLICATE_API_TOKEN": "your-replicate-api-token"
}
}
}
}
Replace /path/to/mcp-imagegen with the absolute path to your cloned repository and your-replicate-api-token with your actual Replicate API token.
Usage
The MCP Image Generator provides a tool called generate_image that can be used to generate images based on text prompts.
Tool: generate_image
Generates an image based on the provided prompt.
Parameters:
prompt(string): The text prompt to generate an image forwidth(number, optional): The width of the image to generate (default: 512)height(number, optional): The height of the image to generate (default: 512)numberOfImages(number, optional): The number of images to generate (default: 1)
Environment Variables
PROVIDER: The provider to use for image generation (default: "replicate")REPLICATE_API_TOKEN: Your Replicate API tokenTOGETHER_API_KEY: Your Together AI API keyMODEL_NAME: The model to use for image generation (default: "black-forest-labs/flux-schnell")
License
MIT
Related Servers
Scout Monitoring MCP
sponsorPut performance and error data directly in the hands of your AI assistant.
Alpha Vantage MCP Server
sponsorAccess financial market data: realtime & historical stock, ETF, options, forex, crypto, commodities, fundamentals, technical indicators, & more
IMAGIN.studio API Docs
Semantic search over IMAGIN.studio vehicle imagery API documentation, CDN configuration, and integration guides.
EChart Server
A Go service that dynamically generates ECharts chart pages from JSON configurations.
OriginUI MCP Server
Search and install OriginUI components, with details fetched dynamically from the OriginUI JSON registry.
Shadcn UI MCP Server
A powerful and flexible MCP server designed to enhance the development experience with Shadcn UI components, providing tools for component management, documentation, and installation.
rxjs-mcp-server
Execute, debug, and visualize RxJS streams directly from AI assistants like Claude.
CAD-Query MCP Server
A server for generating and verifying CAD models using the CAD-Query Python library.
Emcee
An MCP server for any web application with an OpenAPI specification, connecting AI models to external tools and data services.
Multiverse MCP Server
A middleware server for running multiple, isolated instances of MCP servers with unique namespaces and configurations.
Omega Memory
Persistent memory for AI coding agents with semantic search, contradiction detection, memory decay, and cross-session learning. 25 MCP tools, local-first, #1 on LongMemEval (95.4%).
AI pair programming
Orchestrates a dual-AI engineering loop where a Primary AI plans and implements, while a Review AI validates and reviews, with continuous feedback for optimal code quality. Supports custom AI pairing (Claude, Codex, Gemini, etc.)