Create Image MCP Server

A Model Context Protocol (MCP) server that generates and edits images using OpenAI's GPT Image model (gpt-image-1.5). This server enables Claude Desktop, Claude Code, and other MCP clients to create images from text descriptions and edit existing images.

Features

• Text-to-image generation via OpenAI GPT Image model

• Image editing and style transfer with input image support

• Configurable size, quality, and output format

• Transparent background support

• Multiple image variations in a single request

• Images saved to disk with text-only responses (no base64 bloat)

• Retry with exponential backoff for transient failures

Related MCP server: GPT Image MCP Server

Prerequisites

• Node.js >= 20.0.0

• OpenAI API Key

Installation

Option 1: NPM Global Install (Recommended)

npm install -g @gpriday/create-image-mcp

The create-image-mcp command will be available globally.

Option 2: Local Development Install

git clone https://github.com/gpriday/create-image-mcp.git
cd create-image-mcp
npm install

Configuration

Create a .env file in your project root or home directory (~/.env):

OPENAI_API_KEY=your_api_key_here

You can get an OpenAI API key from OpenAI Platform.

For local development, validate your configuration with:

npm run check-env

The server will automatically load .env from:

1. Current working directory (.env)

2. Home directory (~/.env) as fallback

3. Or use environment variables directly

Usage

Run the MCP Server

If installed globally:

create-image-mcp

If running locally:

npm start

The server runs on stdio and communicates via JSON-RPC 2.0.

Test the Server

npm test                     # Unit tests
npm run test:integration     # Integration test with live API
npm run test:all             # All tests

Available Tools

create_image

Generate or edit images using OpenAI GPT Image.

Use when: user says "create an image", "generate a picture", "draw", "make an illustration", "edit an image", "transform a photo", or any visual content creation request.

Parameters:

Examples:

Generate a simple image:

{
  "name": "create_image",
  "arguments": {
    "prompt": "A serene mountain landscape at sunset with golden light",
    "output_file": "./landscape.png"
  }
}

Generate with specific settings:

{
  "name": "create_image",
  "arguments": {
    "prompt": "A futuristic city skyline with flying cars, cyberpunk style",
    "output_file": "./cyberpunk-city.png",
    "size": "1536x1024",
    "quality": "high",
    "number_of_images": 2
  }
}

Generate with transparent background:

{
  "name": "create_image",
  "arguments": {
    "prompt": "A minimalist flat vector logo of an owl",
    "output_file": "./logo.png",
    "background": "transparent"
  }
}

Edit an existing image:

{
  "name": "create_image",
  "arguments": {
    "prompt": "Change the background to a beach scene",
    "input_images": ["./photo.jpg"],
    "output_file": "./edited-photo.png"
  }
}

Style transfer:

{
  "name": "create_image",
  "arguments": {
    "prompt": "Make this image look like a watercolor painting",
    "input_images": ["./source.png"],
    "output_file": "./watercolor.png"
  }
}

Response Format:

The tool saves images to disk and returns a text-only response:

Image saved to: ./landscape.png (245.3 KB, image/png)

For multiple images, files are numbered:

Image saved to: ./cyberpunk-city_1.png (312.1 KB, image/png)
Image saved to: ./cyberpunk-city_2.png (298.7 KB, image/png)

Integration with Claude Desktop

Add this server to your Claude Desktop configuration.

If Installed Globally (Recommended)

macOS

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "create-image": {
      "command": "create-image-mcp",
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Windows

Edit %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "create-image": {
      "command": "create-image-mcp",
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Linux

Edit ~/.config/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "create-image": {
      "command": "create-image-mcp",
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

If Running Locally

macOS

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "create-image": {
      "command": "node",
      "args": ["/path/to/create-image-mcp/src/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Windows

Edit %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "create-image": {
      "command": "node",
      "args": ["C:\\path\\to\\create-image-mcp\\src\\index.js"],
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Linux

Edit ~/.config/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "create-image": {
      "command": "node",
      "args": ["/path/to/create-image-mcp/src/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

After updating the configuration, restart Claude Desktop.

Integration with Claude Code

Option 1: Project-Level mcp.json (Recommended)

Add an mcp.json file to your project root. This is the simplest approach and works automatically when Claude Code opens the project.

Note: If OPENAI_API_KEY is already set in your shell environment (e.g. in ~/.zshrc, ~/.bashrc, or ~/.env), you can omit the env field entirely.

If installed globally:

{
  "mcpServers": {
    "create-image": {
      "command": "create-image-mcp",
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

If running locally:

{
  "mcpServers": {
    "create-image": {
      "command": "node",
      "args": ["/path/to/create-image-mcp/src/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Option 2: CLI Command

For current project only:

claude mcp add --scope project create-image -e OPENAI_API_KEY=your_api_key_here -- create-image-mcp

For your user (available in all projects):

claude mcp add --scope user create-image -e OPENAI_API_KEY=your_api_key_here -- create-image-mcp

Verify the server is running:

claude mcp list

Integration with OpenAI Codex

Add the MCP server using the codex mcp add command or by editing ~/.codex/config.toml.

Using CLI (Recommended)

If installed globally:

codex mcp add create-image --env OPENAI_API_KEY=your_api_key_here -- create-image-mcp

If running locally:

codex mcp add create-image --env OPENAI_API_KEY=your_api_key_here -- node /path/to/create-image-mcp/src/index.js

Manual Configuration

Edit ~/.codex/config.toml:

[mcp.create-image]
command = "create-image-mcp"
env = ["OPENAI_API_KEY=your_api_key_here"]

Development

Dependency Management

• Semver Ranges: Dependencies use caret (^) ranges for automatic patch/minor security updates

• Lockfile: package-lock.json is committed for reproducible builds

• CI/CD: Use npm ci (not npm install) to enforce lockfile versions

• Security: Run npm run security:audit regularly

Project Structure

create-image/
├── src/
│   └── index.js               # Main MCP server
├── scripts/
│   └── check-env.js           # Environment validation
├── test/
│   ├── unit/
│   │   ├── tool-handler.test.js    # Unit tests
│   │   └── tool-description.test.js # Schema tests
│   └── test-create-image-mcp.js    # Integration tests
├── package.json
├── package-lock.json          # Committed for reproducibility
├── .env                       # API key (git-ignored)
├── .env.example               # API key template
├── .gitignore
├── LICENSE
└── README.md

Scripts

Development:

• npm start - Start the MCP server (auto-runs environment validation)

• npm test - Run unit tests

• npm run test:integration - Run integration tests

• npm run test:all - Run all tests

• npm run dev - Run server with auto-reload

Environment & Security:

• npm run check-env - Validate environment configuration

• npm run security:audit - Check for security vulnerabilities

• npm run security:fix - Auto-fix security issues

• npm run security:update - Update dependencies and audit

Error Handling

The server provides categorized error handling:

• Input Validation: Parameters validated for presence, type, length, and enum membership

• [AUTH_ERROR]: Missing or invalid API keys

• [QUOTA_ERROR]: API quota, rate limit, or billing errors

• [TIMEOUT_ERROR]: Request timeout errors

• [SAFETY_ERROR]: Content blocked by safety filters or content policy violations

• [API_ERROR]: General API errors

• Retry Logic: Transient failures retried with exponential backoff (up to 3 attempts)

• Process Stability: Unhandled rejections and exceptions trigger clean shutdown

License

MIT

Contributing

Contributions welcome! Please open an issue or PR.

Support

For issues or questions:

1. Check the MCP documentation

2. Review OpenAI API docs

3. Open an issue in this repository