GPT-Image-1 MCP Server

A Model Context Protocol (MCP) server that provides image generation, editing, and management capabilities using OpenAI's GPT-Image-1 model.

Features

• Image Generation: Generate high-quality images from text prompts
• Image Editing: Edit existing images with new prompts
• Image Management: Store, retrieve, and manage generated images
• History Tracking: Track all image generations with metadata
• Comprehensive Logging: Full logging and error handling
• TypeScript Support: Fully typed with TypeScript

Installation

Via npm (Recommended)

npm install -g @graisol/gpt-image-mcp

From Source

1. Clone the repository:

git clone https://github.com/GRAIsol/gpt-image-mcp.git
cd gpt-image-mcp

2. Install dependencies:

npm install

3. Build the project:

npm run build

Configuration

Command Line Usage

# Using npm global installation
gpt-image-mcp --api-key your_openai_api_key_here

# Using npx
npx @graisol/gpt-image-mcp --api-key your_openai_api_key_here

# Show help
gpt-image-mcp --help

Environment Variables (Optional)

You can also use environment variables for configuration:

# Required (if not provided via --api-key)
OPENAI_API_KEY=your_openai_api_key_here

# Optional
OPENAI_ORG_ID=your_org_id_here
DEFAULT_IMAGE_SIZE=1024x1024
DEFAULT_IMAGE_QUALITY=high
DEFAULT_MODERATION=auto
IMAGE_STORAGE_PATH=./mcp-images
MAX_STORED_IMAGES=100
LOG_LEVEL=2
LOG_FILE=./logs/server.log

Usage

Development Mode

npm run dev

Production Mode

npm run build
npm start

MCP Integration

Claude Code CLI (Recommended)

The Claude Code CLI requires environment variables to be passed using the --env flag.

If you installed the package globally via npm:

claude mcp add gpt-image-mcp gpt-image-mcp --env OPENAI_API_KEY=your_openai_api_key_here

Or using npx:

claude mcp add gpt-image-mcp npx @graisol/gpt-image-mcp --env OPENAI_API_KEY=your_openai_api_key_here

If you built from source:

claude mcp add gpt-image-mcp node /PATH/TO/YOUR/PROJECT/gpt-image-mcp/build/index.js --env OPENAI_API_KEY=your_openai_api_key_here

Replace your_openai_api_key_here with your actual OpenAI API key.

Claude Desktop Configuration

For Claude Desktop, add the following to your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

If you installed via npm:

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "npx",
      "args": ["@graisol/gpt-image-mcp", "--api-key", "your_openai_api_key_here"]
    }
  }
}

Or using environment variables:

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "npx",
      "args": ["@graisol/gpt-image-mcp"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}

If you built from source:

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "node",
      "args": ["/path/to/your/gpt-image-mcp/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}

Cursor IDE

For Cursor IDE, add the following to your .cursor/mcp.json configuration file:

{
  "gpt-image-1": {
    "command": "node",
    "args": ["./build/index.js"],
    "cwd": "/path/to/gpt-image-mcp",
    "env": {
      "OPENAI_API_KEY": "your_openai_api_key_here"
    }
  }
}

Or using the npm global installation:

{
    "mcpServers": {
      "gpt-image-mcp": {
        "command": "npx",
        "args": ["@graisol/gpt-image-mcp@latest", "--api-key", "your-api-key-here"]
      }
    }
  }

VS Code (with MCP Extensions)

For VS Code with MCP support extensions, add the following to your workspace settings (.vscode/settings.json):

{
  "mcp.servers": {
    "gpt-image-1": {
      "command": "node",
      "args": ["/path/to/gpt-image-mcp/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}

Or using the npm global installation:

{
  "mcp.servers": {
    "gpt-image-1": {
      "command": "npx",
      "args": ["@graisol/gpt-image-mcp", "--api-key", "your_openai_api_key_here"]
    }
  }
}

Other IDEs

For other IDEs and editors, refer to their specific MCP integration documentation. Most follow similar JSON configuration patterns with command, args, and env parameters.

MCP Tools

generate_image

Generate images from text prompts.

Parameters:

• prompt (required): Text description of the image
• n (optional): Number of images to generate (1-10, default: 1)
• size (optional): Image size - '1024x1024', '1024x1536', '1536x1024', or 'auto' (default: 1024x1024)
• quality (optional): Image quality - 'low', 'medium', or 'high' (default: medium)
• background (optional): Background type - 'transparent', 'opaque', or 'auto' (default: auto)
• output_compression (optional): Compression level (0-100)
• moderation (optional): Moderation level - 'auto' or 'low' (default: auto)
• output_format (optional): Output format - 'png', 'jpeg', or 'webp' (default: png)

Example:

{
  "prompt": "A futuristic cityscape at sunset with flying cars",
  "n": 2,
  "size": "1536x1024",
  "quality": "high",
  "background": "opaque",
  "output_format": "png"
}

edit_image

Edit existing images with new prompts.

Parameters:

• image (required): Base64 encoded image or image URL
• prompt (required): Text description of desired changes
• mask (optional): Mask PNG for inpainting edits
• n (optional): Number of images to generate (1-10, default: 1)
• size (optional): Output image size - '1024x1024', '1024x1536', '1536x1024', or 'auto'
• quality (optional): Image quality - 'low', 'medium', or 'high'
• background (optional): Background type - 'transparent', 'opaque', or 'auto'
• output_compression (optional): Compression level (0-100)
• moderation (optional): Moderation level - 'auto' or 'low'
• output_format (optional): Output format - 'png', 'jpeg', or 'webp'

Example:

{
  "image": "data:image/png;base64,iVBORw0KGgoAAAANS...",
  "prompt": "Add a rainbow in the sky",
  "size": "1024x1024"
}

get_image_info

Get detailed information about a generated image.

Parameters:

• image_id (required): ID of the image

Example:

{
  "image_id": "img_1640995200000_abc123def456"
}

list_generations

List recent image generations with optional filtering.

Parameters:

• limit (optional): Maximum number of results (default: 10)
• offset (optional): Number of results to skip (default: 0)
• filter (optional): Filter by prompt content

Example:

{
  "limit": 20,
  "offset": 0,
  "filter": "sunset"
}

API Response Format

All tools return responses in the following format:

{
  "content": [
    {
      "type": "text",
      "text": "JSON-formatted result"
    }
  ]
}

Supported Image Sizes

• 1024x1024 (square format, default)
• 1024x1536 (portrait format)
• 1536x1024 (landscape format)
• auto (automatic sizing based on prompt context)

Error Handling

The server provides comprehensive error handling with detailed error messages:

• 400 Bad Request: Invalid request parameters
• 401 Unauthorized: Invalid API key
• 403 Forbidden: Access denied or organization verification required
• 404 Not Found: Model or endpoint not found
• 429 Rate Limited: Too many requests
• 500 Internal Server Error: OpenAI service temporarily unavailable

Storage

Generated images and metadata are stored locally in the configured storage directory:

• ./mcp-images/ - Default storage location
• generation_history.json - Image generation history
• {image_id}.json - Individual image metadata files
• {image_id}.png - Base64 images (if stored locally)

Logging

The server includes comprehensive logging with configurable levels:

• ERROR (0): Error messages only
• WARN (1): Warning and error messages
• INFO (2): Informational, warning, and error messages (default)
• DEBUG (3): All messages including debug information

Development

Project Structure

gpt-image-mcp/
├── src/
│   ├── types/          # TypeScript type definitions
│   ├── utils/          # Utility functions
│   │   ├── openai-client.ts
│   │   ├── image-manager.ts
│   │   └── logger.ts
│   ├── tools/          # MCP tool implementations
│   │   └── index.ts
│   └── index.ts        # Main server entry point
├── build/              # Compiled JavaScript output
├── mcp-images/         # Image storage directory
├── package.json
├── tsconfig.json
└── README.md

Building

npm run build

Running Tests

npm test

License

MIT License

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Support

For issues and questions, please open an issue on the GitHub repository.