imagegen-mcp

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).

• 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:

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.

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

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

1. Open Cursor Settings:

   • Go to File > Preferences > Cursor Settings (or use the shortcut Ctrl+, / Cmd+,).

2. Navigate to MCP Settings:

   • Search for "MCP" in the settings search bar.
   • Find the "Model Context Protocol: Custom Servers" setting.

3. Add Custom Server:

   • Click on "Edit in settings.json".
   • Add a new entry to the mcpServers array. It should look something like this:

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

   • Customize the command:
     • You can change the --models argument in the command field to specify which models you want Cursor to have access to (e.g., --models dall-e-3 or --models gpt-image-1). Make sure your OpenAI API key has access to the selected models.

4. Save Settings:

   • Save the settings.json file.

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).

1. Clone the repository:

   git clone <your-repository-url>
   cd <repository-directory>

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
   

To build the TypeScript code into JavaScript:

npm run build
# or
yarn build

This will compile the code into the dist directory.

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 <model1> <model2> ...: 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).
  • Example using npx (also works for local runs): ... --models gpt-image-1 dall-e-3
  • Example after cloning: node dist/index.js --models dall-e-3 dall-e-2

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

The server exposes the following MCP tools:

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:
  • A text object containing the path to the saved temporary image file (e.g., /tmp/uuid.png).

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:
  • A text object containing the path to the saved temporary image file (e.g., /tmp/uuid.png).

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

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