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DALL-E 3 MCP Server

by chrisurf
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JavaScript
MIT License
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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

  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

Changelog

See CHANGELOG.md for a detailed history of changes.

Related Projects

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
A
security – no known vulnerabilities
A
license - permissive license
A
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

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.

  1. Features
    1. Installation
      1. Using NPX (Recommended)
      2. Using NPM
      3. From Source
    2. Prerequisites
      1. Configuration
        1. Environment Variables
      2. Usage
        1. With Claude Desktop
        2. With Other MCP Clients
      3. Available Tools
        1. generate_image
      4. API Reference
        1. Image Sizes
        2. Quality Options
        3. Style Options
      5. Development
        1. Setup
        2. Available Scripts
        3. Project Structure
        4. Running Tests
      6. Error Handling
        1. Logging
          1. Contributing
            1. Development Workflow
          2. CI/CD
            1. License
              1. Support
                1. Changelog
                  1. Related Projects
                    1. Acknowledgments

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