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Image Generation MCP Server

by mikeyny

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

Related MCP server: Together AI Image Server

Prerequisites

  • Node.js 16+

  • Replicate API token

  • TypeScript SDK for MCP

Setup

  1. Clone the repository

  2. Install dependencies:

    npm install
  3. Add your Replicate API token directly in the code at src/imageService.ts by updating the apiToken constant:

    // 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 .env file in the root directory and set your API token there:

    REPLICATE_API_TOKEN=your-replicate-api-token-here

    Then build the project:

    npm run build

Usage

To use with cursor:

  1. Go to Settings

  2. Select Features

  3. Scroll down to "MCP Servers"

  4. Click "Add new MCP Server"

  5. Set Type to "Command"

  6. 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

Install Server
A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

Resources

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Admins can modify the Dockerfile, update the server description, and track usage metrics. If you are the server author, to access the admin panel.

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