ai-image-gen-mcp
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An MCP server implementation for generating images using Replicate's 'black-forest-labs/flux-schnell' model.
Image Generation MCP Server
An MCP (Model Context Protocol) server implementation for generating images using Replicate's black-forest-labs/flux-schnell model.
Ideally to be used with Cursor's MCP feature, but can be used with any MCP client.
Features
- Generate images from text prompts
- Configurable image parameters (resolution, aspect ratio, quality)
- Save generated images to specified directory
- Full MCP protocol compliance
- Error handling and validation
Prerequisites
- Node.js 16+
- Replicate API token
- TypeScript SDK for MCP
Setup
-
Clone the repository
-
Install dependencies:
npm install -
Add your Replicate API token directly in the code at
src/imageService.tsby updating theapiTokenconstant:// No environment variables are used since they can't be easily set in cursor const apiToken = "your-replicate-api-token-here";Note: If using with Claude, you can create a
.envfile in the root directory and set your API token there:REPLICATE_API_TOKEN=your-replicate-api-token-hereThen build the project:
npm run build
Usage
To use with cursor:
- Go to Settings
- Select Features
- Scroll down to "MCP Servers"
- Click "Add new MCP Server"
- Set Type to "Command"
- Set Command to:
node ./path/to/dist/server.js
API Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
prompt | string | Yes | - | Text prompt for image generation |
output_dir | string | Yes | - | Server directory path to save generated images |
go_fast | boolean | No | false | Enable faster generation mode |
megapixels | string | No | "1" | Resolution quality ("1", "2", "4") |
num_outputs | number | No | 1 | Number of images to generate (1-4) |
aspect_ratio | string | No | "1:1" | Aspect ratio ("1:1", "4:3", "16:9") |
output_format | string | No | "webp" | Image format ("webp", "png", "jpeg") |
output_quality | number | No | 80 | Compression quality (1-100) |
num_inference_steps | number | No | 4 | Number of denoising steps (4-20) |
Example Request
{
"prompt": "black forest gateau cake spelling out 'FLUX SCHNELL'",
"output_dir": "/var/output/images",
"filename": "black_forest_cake",
"output_format": "webp"
"go_fast": true,
"megapixels": "1",
"num_outputs": 2,
"aspect_ratio": "1:1"
}
Example Response
{
"image_paths": [
"/var/output/images/output_0.webp",
"/var/output/images/output_1.webp"
],
"metadata": {
"model": "black-forest-labs/flux-schnell",
"inference_time_ms": 2847
}
}
Error Handling
The server handles the following error types:
- Validation errors (invalid parameters)
- API errors (Replicate API issues)
- Server errors (filesystem, permissions)
- Unknown errors (unexpected issues)
Each error response includes:
- Error code
- Human-readable message
- Detailed error information
License
ISC
