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mikeyny

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

One-click Deploy
A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

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