MCP OpenAI Image Generation Server

This project provides a server implementation based on the Model Context Protocol (MCP) that acts as a wrapper around OpenAI's Image Generation and Editing APIs (see OpenAI documentation).

Features

• Exposes OpenAI image generation capabilities through MCP tools.
• Supports `text-to-image` generation using models like DALL-E 2, DALL-E 3, and gpt-image-1 (if available/enabled).
• Supports `image-to-image` editing using DALL-E 2 and gpt-image-1 (if available/enabled).
• Configurable via environment variables and command-line arguments.
• Handles various parameters like size, quality, style, format, etc.
• Saves generated/edited images to temporary files and returns the path along with the base64 data.

Here's an example of generating an image directly in Cursor using the `text-to-image` tool integrated via MCP:

Quick Run with npx

You can run the server directly from npm using `npx` (requires Node.js and npm):

npx imagegen-mcp [options]

See the Running the Server section for more details on options and running locally.

Prerequisites

• Node.js (v18 or later recommended)
• npm or yarn
• An OpenAI API key

Integration with Cursor

You can easily integrate this server with Cursor to use its image generation capabilities directly within the editor:

1. Open Cursor Settings:

2. Navigate to MCP Settings:

3. Add Custom Server:

"mcpServers": [
        "image-generator-gpt-image": {
            "command": "npx imagegen-mcp --models gpt-image-1",
            "env": {
                "OPENAI_API_KEY": "xxx"
            }
        }
      // ... any other custom servers ...
    ]

4. Save Settings:

Cursor should now recognize the "OpenAI Image Gen" server, and its tools (`text-to-image`, `image-to-image`) will be available in the MCP tool selection list (e.g., when using `@` mention in chat or code actions).

Setup

1. Clone the repository:

git clone 
    cd

2. Install dependencies:

npm install
    # or
    yarn install

3. Configure Environment Variables:

Create a `.env` file in the project root by copying the example:

cp .env.example .env

Edit the `.env` file and add your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

Building

To build the TypeScript code into JavaScript:

npm run build
# or
yarn build

This will compile the code into the `dist` directory.

Running the Server

This section provides details on running the server locally after cloning and setup. For a quick start without cloning, see the Quick Run with npx section.

Using ts-node (for development):

npx ts-node src/index.ts [options]

Using the compiled code:

node dist/index.js [options]

Options:

• `--models ...`: Specify which OpenAI models the server should allow. If not provided, it defaults to allowing all models defined in `src/libs/openaiImageClient.ts` (currently gpt-image-1, dall-e-2, dall-e-3).

The server will start and listen for MCP requests via standard input/output (using `StdioServerTransport`).

MCP Tools

The server exposes the following MCP tools:

`text-to-image`

Generates an image based on a text prompt.

Parameters:

• `text` (string, required): The prompt to generate an image from.
• `model` (enum, optional): The model to use (e.g., `gpt-image-1`, `dall-e-2`, `dall-e-3`). Defaults to the first allowed model.
• `size` (enum, optional): Size of the generated image (e.g., `1024x1024`, `1792x1024`). Defaults to `1024x1024`. Check OpenAI documentation for model-specific size support.
• `style` (enum, optional): Style of the image (`vivid` or `natural`). Only applicable to `dall-e-3`. Defaults to `vivid`.
• `output_format` (enum, optional): Format (`png`, `jpeg`, `webp`). Defaults to `png`.
• `output_compression` (number, optional): Compression level (0-100). Defaults to 100.
• `moderation` (enum, optional): Moderation level (`low`, `auto`). Defaults to `low`.
• `background` (enum, optional): Background (`transparent`, `opaque`, `auto`). Defaults to `auto`. `transparent` requires `output_format` to be `png` or `webp`.
• `quality` (enum, optional): Quality (`standard`, `hd`, `auto`, ...). Defaults to `auto`. `hd` only applicable to `dall-e-3`.
• `n` (number, optional): Number of images to generate. Defaults to 1. Note: `dall-e-3` only supports `n=1`.

Returns:

• `content`: An array containing:

`image-to-image`

Edits an existing image based on a text prompt and optional mask.

Parameters:

• `images` (string, required): An array of *file paths* to local images.
• `prompt` (string, required): A text description of the desired edits.
• `mask` (string, optional): A *file path* of mask image (PNG). Transparent areas indicate where the image should be edited.
• `model` (enum, optional): The model to use. Only `gpt-image-1` and `dall-e-2` are supported for editing. Defaults to the first allowed model.
• `size` (enum, optional): Size of the generated image (e.g., `1024x1024`). Defaults to `1024x1024`. `dall-e-2` only supports `256x256`, `512x512`, `1024x1024`.
• `output_format` (enum, optional): Format (`png`, `jpeg`, `webp`). Defaults to `png`.
• `output_compression` (number, optional): Compression level (0-100). Defaults to 100.
• `quality` (enum, optional): Quality (`standard`, `hd`, `auto`, ...). Defaults to `auto`.
• `n` (number, optional): Number of images to generate. Defaults to 1.

Returns:

• `content`: An array containing:

Development

• Linting: `npm run lint` or `yarn lint`
• Formatting: `npm run format` or `yarn format` (if configured in `package.json`)

Contributing

Pull Requests (PRs) are welcome! Please feel free to submit improvements or bug fixes.