DALL-E 3 MCP Server

CI/CD Pipeline npm version License Node.js Version

A Model Context Protocol (MCP) server that provides DALL-E 3 image generation capabilities. This server allows LLMs to generate high-quality images using OpenAI's DALL-E 3 model through the standardized MCP interface.

Features

🎨 High-Quality Image Generation: Uses DALL-E 3 for state-of-the-art image creation
🔧 Flexible Configuration: Support for different sizes, quality levels, and styles
📁 Automatic File Management: Handles directory creation and file saving
🛡️ Robust Error Handling: Comprehensive error handling with detailed feedback
📊 Detailed Logging: Comprehensive logging for debugging and monitoring
🚀 TypeScript: Fully typed for better development experience
🧪 Well Tested: Comprehensive test suite with high coverage

Installation

Using NPX (Recommended)

npx imagegen-mcp-d3

Using NPM

npm install -g imagegen-mcp-d3

From Source

git clone https://github.com/chrisurf/imagegen-mcp-d3.git
cd imagegen-mcp-d3
npm install
npm run build
npm start

Prerequisites

Node.js: Version 18.0.0 or higher
OpenAI API Key: You need a valid OpenAI API key with DALL-E 3 access

Configuration

Environment Variables

Set your OpenAI API key as an environment variable:

export OPENAI_API_KEY="your-openai-api-key-here"

Or create a .env file in your project root:

OPENAI_API_KEY=your-openai-api-key-here

Usage

With Claude Desktop

Add this server to your Claude Desktop configuration:

{
  "mcpServers": {
    "imagegen-mcp-d3": {
      "command": "npx",
      "args": ["imagegen-mcp-d3"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

With Other MCP Clients

The server implements the standard MCP protocol and can be used with any compatible client.

Available Tools

`generate_image`

Generates an image using DALL-E 3 and saves it to the specified location.

Parameters:

prompt (required): Text description of the image to generate
output_path (required): Full file path where the image should be saved
size (optional): Image dimensions - "1024x1024", "1024x1792", or "1792x1024" (default: "1024x1024")
quality (optional): Image quality - "standard" or "hd" (default: "hd")
style (optional): Image style - "vivid" or "natural" (default: "vivid")

Example:

{
  "name": "generate_image",
  "arguments": {
    "prompt": "A serene sunset over a mountain lake with pine trees",
    "output_path": "/Users/username/Pictures/sunset_lake.png",
    "size": "1024x1792",
    "quality": "hd",
    "style": "natural"
  }
}

Response:

The tool returns detailed information about the generated image, including:

Original and revised prompts
Image URL
File save location
Image specifications
File size

API Reference

Image Sizes

Square: 1024x1024 - Perfect for social media and general use
Portrait: 1024x1792 - Great for mobile wallpapers and vertical displays
Landscape: 1792x1024 - Ideal for desktop wallpapers and horizontal displays

Quality Options

Standard: Faster generation, good quality
HD: Higher quality with more detail (recommended)

Style Options

Vivid: More dramatic and artistic interpretations
Natural: More realistic and natural-looking results

Development

Setup

git clone https://github.com/chrisurf/imagegen-mcp-d3.git
cd imagegen-mcp-d3
npm install

Available Scripts

npm run dev          # Run in development mode with hot reload
npm run build        # Build for production
npm run start        # Start the built server
npm run test         # Run tests
npm run test:watch   # Run tests in watch mode
npm run test:coverage # Run tests with coverage report
npm run lint         # Run ESLint
npm run lint:fix     # Fix ESLint issues
npm run format       # Format code with Prettier
npm run typecheck    # Run TypeScript type checking

Project Structure

src/
├── index.ts           # Main server implementation
├── types.ts          # TypeScript type definitions
└── __tests__/        # Test files
    └── index.test.ts # Main test suite

Running Tests

# Run all tests
npm test

# Run tests with coverage
npm run test:coverage

# Run tests in watch mode during development
npm run test:watch

Error Handling

The server provides comprehensive error handling for common scenarios:

Missing API Key: Clear error message when OPENAI_API_KEY is not set
Invalid Parameters: Validation errors for required and optional parameters
API Errors: Detailed error messages from the OpenAI API
File System Errors: Handling of directory creation and file writing issues
Network Errors: Graceful handling of network connectivity issues

Logging

The server provides detailed logging for monitoring and debugging:

Request initiation and parameters
API communication status
Image generation progress
File saving confirmation
Error details and stack traces

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Workflow

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Make your changes
Add tests for new functionality
Ensure all tests pass: npm test
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

CI/CD

This project uses GitHub Actions for continuous integration and deployment:

Testing: Automated testing on multiple Node.js versions (18, 20, 22)
Code Quality: ESLint, Prettier, and TypeScript checks
Security: Dependency vulnerability scanning
Publishing: Automatic NPM publishing on release
Coverage: Local code coverage reporting

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: Open an issue for support

Changelog

See CHANGELOG.md for a detailed history of changes.

Model Context Protocol - The official MCP specification
MCP TypeScript SDK - TypeScript SDK for MCP
Claude Desktop - AI assistant that supports MCP servers

Acknowledgments

OpenAI for the DALL-E 3 API
Anthropic for the Model Context Protocol specification
The MCP community for tools and documentation High-performance MCP for generating images using DALL·E 3 – optimized for fast, scalable, and customizable inference workflows.

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

generate_image

A Model Context Protocol server that provides OpenAI's DALL-E 3 image generation capabilities, allowing LLMs to generate high-quality images through a standardized interface.

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