I'm fucking excited to share our latest open-source project: GPT-Image-1 MCP Server - a production-ready implementation that bridges OpenAI's powerful image generation capabilities with AI assistants like Claude through the Model Context Protocol.
The Problem: Siloed AI Capabilities
Different AI systems excel at different tasks. Claude might be your go-to for code generation and analysis, while OpenAI's GPT-Image-1 leads in image generation. But what if you could seamlessly use both capabilities in a single workflow?
That's where the Model Context Protocol (MCP) comes in - and why we built this server.
What is GPT-Image-1 MCP?
The GPT-Image-1 MCP Server is a TypeScript-based implementation that exposes OpenAI's image generation capabilities as structured tools that any MCP-compatible AI assistant can use. Think of it as a universal adapter that lets different AI systems work together.
Key Features That Matter
🎨 Advanced Image Generation
- Text-to-image generation with GPT-Image-1
- Multiple size options (1024x1024, 1024x1536, 1536x1024)
- Quality control (high, medium, low)
- Background transparency support
- Built-in content moderation
📦 Enterprise-Ready Architecture
- Full TypeScript with strict typing
- Comprehensive error handling
- Multi-level logging (ERROR, WARN, INFO, DEBUG)
- Local storage with configurable limits
- Generation history tracking
🚀 Developer-Friendly Integration
- Published on npm:
@graisol/gpt-image-mcp - One-line installation
- Support for Claude Desktop, Claude Code CLI, Cursor, VS Code
- Environment variable configuration
Real-World Usage Example
Here's how simple it is to use once installed:
// In Claude or any MCP-compatible assistant
"Generate an image of a futuristic city at sunset with flying cars"
// The MCP server handles:
// - API authentication
// - Request formatting
// - Image generation
// - Local storage
// - Metadata tracking
// - Error handling
Technical Architecture Deep Dive
The MCP Advantage
The Model Context Protocol provides a standardized way for AI assistants to interact with external tools. Our implementation leverages this to create a clean separation between the AI interface and the OpenAI API integration.
// Simplified tool registration
server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [
{
name: "generate_image",
description: "Generate images from text prompts",
inputSchema: {
type: "object",
properties: {
prompt: { type: "string" },
size: { enum: ["1024x1024", "1024x1536", "1536x1024"] },
quality: { enum: ["high", "medium", "low"] }
}
}
}
]
}));
Storage and History Management
Every generated image is stored locally with comprehensive metadata:
{
"id": "img_1234567890",
"prompt": "futuristic city at sunset",
"size": "1536x1024",
"quality": "high",
"created": 1234567890,
"model": "gpt-image-1",
"filePath": "./images/img_1234567890.png"
}
Installation in 60 Seconds
Global Installation
npm install -g @graisol/gpt-image-mcp
Claude Code CLI Integration
claude mcp add gpt-image-mcp gpt-image-mcp --env OPENAI_API_KEY=your_key
Configuration for Other Platforms
Claude Desktop
Edit your claude_desktop_config.json:
{
"mcpServers": {
"gpt-image-mcp": {
"command": "npx",
"args": ["@graisol/gpt-image-mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key"
}
}
}
}
VS Code & Cursor IDE
Add to your .cursor/mcp.json, .vscode/mcp.json.vs or settings:
{
"mcpServers": {
"gpt-image-mcp": {
"command": "npx",
"args": ["@graisol/gpt-image-mcp@latest", "--api-key", "your-api-key-here"]
}
}
}
Why This Matters
1. Unified AI Workflows
No more context switching between different AI platforms. Generate images while coding, writing, or analyzing - all in one place.
2. Enterprise Control
- Local storage means your images stay on your infrastructure
- Comprehensive logging for audit trails
- Configurable storage limits
- Organization ID support for OpenAI teams
3. Open Source Innovation
The entire codebase is open source, encouraging community contributions and customizations for specific use cases.
What's Next?
We're already working on exciting enhancements:
- Image Editing Support - Modify existing images with AI
- Batch Generation - Generate multiple variations efficiently
- Cloud Storage Integration - S3, GCS, and Azure Blob support
- Advanced Caching - Reduce API costs with intelligent caching
Building Your Own MCP Server?
Our implementation showcases MCP best practices:
- Proper Error Handling: Every edge case considered
- Type Safety: Full TypeScript with strict mode
- Logging: Multi-level logging for debugging
- Testing: Comprehensive test coverage
- Documentation: Clear, actionable docs
Check out the source code on GitHub for implementation details.
The Bigger Picture
The Model Context Protocol represents a paradigm shift in how we think about AI integration. Instead of monolithic AI systems trying to do everything, we can create specialized tools that work together seamlessly.
The GPT-Image-1 MCP Server is just one example of this philosophy in action. Imagine a future where every AI capability is accessible through standardized protocols, enabling truly modular and powerful AI workflows.
Questions? Issues? Feature requests? Visit our GitHub repository or reach out to our team.
This is the future of AI integration - modular, powerful, and developer-friendly. Join us in building the next generation of AI tools.
