MCP Image Generation Server

A Model Context Protocol (MCP) server for image generation using multiple AI providers including Tencent Hunyuan, OpenAI DALL-E 3, and Doubao APIs.

Features

🎯 Multi-API Provider Support

• Tencent Hunyuan: 18 artistic styles with Chinese optimization

• OpenAI DALL-E 3: High-quality image generation with English optimization

• Doubao (ByteDance): Balanced quality and speed with 12 styles

🚀 Core Features

• Generate images from text descriptions

• Support for multiple image styles across different providers

• Support for different image resolutions

• Negative prompts for excluding unwanted elements

• Intelligent provider selection and management

• Unified parameter format with provider-specific options

🔧 Smart Provider Management

• Automatic detection of available API providers

• Support for specifying particular providers or automatic selection

• Unified error handling and retry mechanisms

• Flexible parameter formats: provider:style and provider:resolution

Installation

Using UV (Recommended)

UV is a fast, modern Python package manager. Recommended usage:

Using Traditional pip

If you prefer traditional pip:

Environment Setup

Create a .env file in the project root with the following content:

Usage

🔄 Choosing Your Server Version

This project offers two server implementations:

Single API Server (Original)

Multi-API Server (New - Recommended)

Recommendation: Use the multi-API server (mcp_image_server_multi.py) for access to all supported providers and enhanced features.

Running the MCP Server

You can run the MCP server as follows:

Screenshot of MCP server running successfully:

Connecting to the Server

You can connect from MCP-compatible client(recommand cursor now). The server provides the following features:

Resources

• styles://list - List all available image styles

• resolutions://list - List all available image resolutions

Tools

• generate_image - Generate images based on prompt, style, and resolution

Prompts

• image_generation_prompt - Create image generation prompt templates

🎨 Multi-API Usage Examples

Basic Usage

Advanced Parameter Usage

📊 Supported Providers and Parameters

Tencent Hunyuan

• Styles: 18 options including riman, xieshi, shuimo, saibopengke, youhua

• Resolutions: 8 options from 768:768 to 1280:720

• Specialty: Chinese-optimized, rich artistic styles

OpenAI DALL-E 3

• Styles: 12 options including natural, vivid, realistic, artistic, anime

• Resolutions: 7 options including ultra-high resolution 1792x1024

• Specialty: High-quality output, English optimization

Doubao (ByteDance)

• Styles: 12 options including general, anime, chinese_painting, cyberpunk

• Resolutions: 9 options from 512x512 to 1024x576

• Specialty: Balanced quality and speed

Cursor Integration

To add this MCP server in Cursor:

1. Open Cursor

2. Go to Settings > Features > MCP

3. Click "+ Add New MCP Server"

4. Fill in the configuration:

   • Name: Multi-API Image Generator (or any descriptive name)

   • Type: stdio

   • Command: Full command, must include the absolute path to Python and the script

Single API Configuration (Original)

Multi-API Configuration (Recommended)

Environment Variables

When configuring the MCP server in Cursor, set the following environment variables:

For Single API (Hunyuan only):

• TENCENT_SECRET_ID: Your Tencent Cloud API Secret ID

• TENCENT_SECRET_KEY: Your Tencent Cloud API Secret Key

• MCP_IMAGE_SAVE_DIR: Your save image dir, e.g.: D:\data\mcp_img

For Multi-API (All providers):

• TENCENT_SECRET_ID: Your Tencent Cloud API Secret ID

• TENCENT_SECRET_KEY: Your Tencent Cloud API Secret Key

• OPENAI_API_KEY: Your OpenAI API Key

• DOUBAO_ACCESS_KEY: Your Doubao Access Key

• DOUBAO_SECRET_KEY: Your Doubao Secret Key

• MCP_IMAGE_SAVE_DIR: Your save image dir, e.g.: D:\data\mcp_img

• OPENAI_BASE_URL: (Optional) Custom OpenAI endpoint

• DOUBAO_ENDPOINT: (Optional) Custom Doubao endpoint

Note: You only need to configure the API keys for the providers you want to use. The system will automatically detect available providers.

🎯 Multi-API Usage in Cursor

With the multi-API server, you can use natural language in Cursor to specify different providers:

Verification

1. Save the configuration

2. Restart Cursor

3. Start a new chat and enter: "Generate a mountain landscape image"

4. If everything is set up correctly, the AI will use your MCP server to generate the image

Note: The first time you use it, Cursor may ask for permission to use this MCP server.

Let's look at the steps in Cursor:

• step_1: types your generate command in cursor

  

• step_2: after your approval it will call the mcp image-gen tool and save it

  

• Step 3: View or use the image saved in the directory (MCP_IMAGE_SAVE_DIR) you have set in the .env file

  

You can also ask Cursor to design images for your website ✨. Cursor can use the MCP tool to generate images that match your specific layout requirements 🎨. Perfect for creating beautiful web designs!

Tip: You don't need to manually move the generated images from the save directory to your project directory. Cursor will handle this automatically after your approval. This is one of the main advantages of using Cursor.

• Planning the move

  

• Executing the move

  

• Example Performance

  Original web design:

  New design after generating and moving the image to the project using Cursor:

Troubleshooting

• Ensure environment variables are set correctly

• Check for spaces in paths; use quotes if needed

• Ensure the virtual environment is activated (if using one)

• Try running the server script directly to check for errors

• Check UV environment with uv --version

Front-end Demo

For a front-end integration example, see web-design-demo/. This example demonstrates how to develop a real project using Cursor IDE, where you can generate and manage images directly within your development environment using our MCP tool 🛠️. No need to switch between different image generation tools or leave your IDE - everything can be done right in your development workflow ✨.

• screenshot of the demo web

API Reference

Multi-API Architecture

The project now supports multiple image generation APIs through a unified interface:

Supported APIs

1. Tencent Hunyuan Image Generation API (Original)

2. OpenAI DALL-E 3 API (New)

3. Doubao Image Generation API (New)

Unified MCP Resources

• providers://list - List all available providers

• styles://list - List all styles from all providers

• resolutions://list - List all resolutions from all providers

• styles://provider/{provider_name} - Get styles for specific provider

• resolutions://provider/{provider_name} - Get resolutions for specific provider

Enhanced MCP Tools

• generate_image - Multi-provider image generation with intelligent routing

Tencent Hunyuan Image Generation API

The project originally used and continues to support Tencent Hunyuan Image Generation API. Here are the key details:

API Endpoints

• Domain: hunyuan.tencentcloudapi.com

• Region: ap-guangzhou (Currently only supports Guangzhou region)

• Default API Rate Limit: 20 requests/second

• Concurrent Tasks: Default 1 task at a time

Task Flow

1. Submit Task: Submit an asynchronous image generation task with text description

2. Query Task: Get task status and results using task ID

3. Result URL: Generated image URLs are valid for 1 hour

For detailed API documentation and pricing, please refer to:

• API Documentation

• Pricing Details

OpenAI DALL-E 3 API

API Features

• High-quality image generation

• Automatic prompt optimization

• Multiple style options

• High-resolution output support

Doubao API (ByteDance)

API Features

• ByteDance's proprietary image generation model

• Balanced quality and speed

• Chinese and English prompt support

• Multiple artistic styles

RoadMap

• Current Version

  • ✅ Tencent Hunyuan image generation API

  • ✅ OpenAI DALL-E 3 API integration

  • ✅ Doubao API integration

  • ✅ Multi-provider management system

  • ✅ Intelligent provider selection

  • ✅ Unified parameter interface

• Future Plans

  • Support more mainstream text-to-image model APIs, including:

    • Alibaba Tongyi Wanxiang

    • Baidu ERNIE-ViLG

    • Stable Diffusion API

  • Advanced features:

    • Image editing and modification

    • Batch image generation

    • Style transfer capabilities

    • Custom model fine-tuning support

  • Enhanced MCP integration:

    • Real-time generation progress

    • Image history and management

    • Advanced prompt templates

Community contributions for more model integrations and new features are welcome!

Compatibility

• This project has been verified to work with the Cursor and Windsurf IDE MCP integration.

  • windsurf is also supported to integrated now

    • screenshot of mcp tool call in windsurf

    • 

    • and the result as follows

    • 

• Future plans include supporting more IDEs and development environments compatible with the Model Context Protocol (MCP).

Acknowledgments

This project is built with FastMCP as the core framework, a powerful implementation of the Model Context Protocol. The MCP integration is based on:

• FastMCP: A fast, Pythonic way to build MCP servers

• MCP Python SDK: The official Python SDK for Model Context Protocol

We also use these excellent open-source projects:

• UV: A fast Python package installer and resolver

• Python-dotenv: Reads key-value pairs from .env file

• Tencentcloud-sdk-python: Official Tencent Cloud SDK for Python

Contributing

We welcome contributions of all kinds! Here are some ways you can help:

• 🐛 Report bugs and issues

• 💡 Suggest new features or improvements

• 🔧 Submit pull requests

• 🎨 Add support for more image generation models

Getting Started with Contributing

1. Fork the repository

2. Create your feature branch (git checkout -b feature/AmazingFeature)

3. Commit your changes (git commit -m 'feat: add some AmazingFeature')

4. Push to the branch (git push origin feature/AmazingFeature)

5. Open a Pull Request

Please make sure to update tests as appropriate and follow the existing coding style.

We appreciate your interest in making this project better!