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

$3

``bash
npx imagegen-mcp-d3
`

$3

`bash
npm install -g imagegen-mcp-d3
`

$3

`bash
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

$3

Set your OpenAI API key as an environment variable:

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

Or create a .env file in your project root:

`env
OPENAI_API_KEY=your-openai-api-key-here
`

Usage

$3

Add this server to your Claude Desktop configuration:

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

$3

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

Available Tools

$3

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:

`json
{
"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

$3

- 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

$3

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

$3

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

Development

$3

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

$3

`bash
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
`

$3

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

$3

`bash


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.

$3

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass: npm test
6. Commit your changes: git commit -m 'Add amazing feature'
7. Push to the branch: git push origin feature/amazing-feature`
8. 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.

Related Projects

- 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.