GPT-Image-1 MCP Server

A Model Context Protocol (MCP) server that provides access to OpenAI's GPT-Image-1 model. This server enables high-quality image generation using OpenAI's latest image generation model with enhanced detail, richer lighting, and fewer artifacts.

🔗 Repository: https://github.com/PierrunoYT/gpt-image-1-mcp-server

🚀 Ready to use! Pre-built executable included - no compilation required.

✅ Enhanced Reliability: Server handles missing API keys gracefully without crashes and includes robust error handling.

Features

High-Quality Image Generation: Uses OpenAI's GPT-Image-1 model
Automatic Image Download: Generated images are automatically saved to local images directory as PNG files
Multiple Sizes: Support for 1024x1024, 1024x1536, and 1536x1024 resolutions
Batch Generation: Generate up to 4 images at once
Style Variations: Enhanced tool with style guidance support
Quality Control: Standard and HD quality options
Base64 Processing: Handles OpenAI's base64 image format automatically
Detailed Responses: Returns local file paths with generation metadata
Robust Error Handling: Graceful handling of missing API keys without server crashes
Universal Portability: Works anywhere with npx - no local installation required
Enhanced Reliability: Graceful shutdown handlers and comprehensive error reporting

Prerequisites

Node.js 18 or higher
OpenAI API key with access to GPT-Image-1 model

Installation

1. Get your OpenAI API Key

Visit OpenAI Platform
Sign up for an account or log in
Navigate to API Keys section
Generate an API key
Ensure you have access to the GPT-Image-1 model

2. Clone or Download

git clone https://github.com/PierrunoYT/gpt-image-1-mcp-server.git
cd gpt-image-1-mcp-server

3. Install Dependencies (Optional)

The server is pre-built, but if you want to modify it:

npm install
npm run build

Configuration

🚀 Recommended: Universal npx Configuration (Works Everywhere)

Best option for portability - works on any machine with Node.js:

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "npx",
      "args": [
        "-y",
        "https://github.com/PierrunoYT/gpt-image-1-mcp-server.git"
      ],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

Benefits:

✅ Universal Access: Works on any machine with Node.js
✅ No Local Installation: npx downloads and runs automatically
✅ Always Latest Version: Pulls from GitHub repository
✅ Cross-Platform: Windows, macOS, Linux compatible
✅ Settings Sync: Works everywhere you use your MCP client

Alternative: Local Installation

If you prefer to install locally, use the path helper:

npm run get-path

This will output the complete MCP configuration with the correct absolute path.

For Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "node",
      "args": ["path/to/gpt-image-1-mcp-server/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

For Kilo Code MCP Settings

Add to your MCP settings file at: C:\Users\[username]\AppData\Roaming\Kilo-Code\MCP\settings\mcp_settings.json

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "node",
      "args": ["path/to/gpt-image-1-mcp-server/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Available Tools

`gpt_image_1_generate`

Generate images using OpenAI's GPT-Image-1 model with standard processing.

Parameters:

prompt (required): Text description of the image to generate
size (optional): "1024x1024", "1024x1536", or "1536x1024" (default: "1024x1024")
n (optional): Number of images to generate, 1-4 (default: 1)

Response includes:

Local file paths for saved PNG images
Generation metadata (revised prompts if applicable)
File information and save status

`gpt_image_1_generate_with_variations`

Generate images using GPT-Image-1 with enhanced style control and quality options.

Parameters:

prompt (required): Text description of the image to generate
size (optional): "1024x1024", "1024x1536", or "1536x1024" (default: "1024x1024")
n (optional): Number of images to generate, 1-4 (default: 1)
style (optional): Style guidance for the image generation
quality (optional): "standard" or "hd" (default: "standard")

Use this tool when:

You want to specify a particular artistic style
You need higher quality (HD) images
You want enhanced control over the generation process
Creating variations of a concept with different styles

Features:

Style-enhanced prompts for better artistic control
HD quality option for higher resolution details
Enhanced metadata in responses

📥 How Image Download Works

The GPT-Image-1 MCP server automatically processes and saves generated images to your local machine. Here's the complete process:

1. Image Generation Flow

API Call: Server calls OpenAI's GPT-Image-1 API
Response: OpenAI returns base64-encoded image data
Auto-Save: Server immediately converts and saves images as PNG files
Response: Returns local file paths and generation metadata

2. Base64 Processing Implementation

Save Function (`saveBase64Image`):

async function saveBase64Image(b64Data: string, filename: string): Promise<string> {
  // 1. Create 'images' directory if it doesn't exist
  const imagesDir = path.join(process.cwd(), 'images');
  if (!fs.existsSync(imagesDir)) {
    fs.mkdirSync(imagesDir, { recursive: true });
  }
  
  // 2. Convert base64 to buffer and save as PNG
  const filePath = path.join(imagesDir, filename);
  const buffer = Buffer.from(b64Data, 'base64');
  fs.writeFileSync(filePath, buffer);
  
  return filePath;
}

Filename Generation (`generateImageFilename`):

function generateImageFilename(prompt: string, index: number): string {
  // Creates safe filename: gpt_image_1_prompt_index_timestamp.png
  const safePrompt = prompt
    .toLowerCase()
    .replace(/[^a-z0-9\s]/g, '')  // Remove special characters
    .replace(/\s+/g, '_')         // Replace spaces with underscores
    .substring(0, 50);            // Limit length
  
  const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
  return `gpt_image_1_${safePrompt}_${index}_${timestamp}.png`;
}

3. File Storage Details

Directory Structure:

your-project/
├── images/                    # Auto-created directory
│   ├── gpt_image_1_mountain_landscape_1_2025-06-24T18-30-45-123Z.png
│   ├── gpt_image_1_cute_robot_1_2025-06-24T18-31-20-456Z.png
│   └── ...

Filename Format:

Prefix: gpt_image_1_
Prompt: First 50 chars, sanitized (alphanumeric + underscores)
Index: Image number (for multiple images)
Timestamp: ISO timestamp for uniqueness
Extension: .png

4. Response Format

The server returns detailed information about saved images:

Successfully generated 1 image(s) using GPT-Image-1:

Prompt: "a serene mountain landscape"
Size: 1024x1024
Number of Images: 1

Generated Images:
Image 1:
  Local Path: /path/to/project/images/gpt_image_1_a_serene_mountain_landscape_1_2025-06-24T18-30-45-123Z.png
  Revised Prompt: A serene mountain landscape with snow-capped peaks...

Images have been saved to the local 'images' directory.

5. Benefits of Local Processing

✅ Persistent Storage: Images saved locally as standard PNG files ✅ Offline Access: View images without internet connection ✅ Organized Storage: All images in dedicated images directory ✅ Unique Naming: No filename conflicts with timestamp system ✅ Standard Format: PNG files compatible with all image viewers ✅ No URL Expiration: Local files never expire unlike temporary URLs

Example Usage

Basic Image Generation

Generate a photorealistic image of a golden retriever playing in a field of sunflowers

With Specific Parameters

Generate an image with:
- Prompt: "A minimalist logo design for a tech startup, clean lines"
- Size: 1536x1024
- Number of images: 2

Advanced Usage with Style

Generate an image of "A futuristic cityscape at night with neon lights and flying cars" 
with style "cyberpunk art" and HD quality

Technical Details

Architecture

Language: TypeScript with ES2022 target
Runtime: Node.js 18+ with ES modules
Protocol: Model Context Protocol (MCP) SDK v1.0.0
API Client: OpenAI JavaScript SDK v4.0.0
Validation: Zod schema validation

API Model Used

Model: gpt-image-1 (OpenAI's latest image generation model)
Response Format: b64_json (base64-encoded PNG data)
Supported Sizes: 1024x1024, 1024x1536, 1536x1024

Error Handling

Graceful API key handling: Server continues running even without OPENAI_API_KEY set
No crash failures: Removed process.exit() calls that caused connection drops
Null safety checks: All tools validate API client availability before execution
Graceful shutdown: Proper SIGINT and SIGTERM signal handling
API error catching: Comprehensive error reporting with detailed context
File save error handling: Graceful fallback with detailed error messages
User-friendly messages: Clear error descriptions instead of technical crashes

Development

Project Structure

├── src/
│   └── index.ts          # Main MCP server implementation
├── build/                # Compiled JavaScript (ready to use)
├── test-server.js        # Server testing utility
├── get-path.js          # Configuration path helper
├── example-mcp-config.json # Example configuration
├── package.json         # Project metadata and dependencies
└── tsconfig.json        # TypeScript configuration

Scripts

npm run build - Compile TypeScript to JavaScript
npm run dev - Watch mode for development
npm run start - Start the server directly
npm run test - Test server startup and basic functionality
npm run get-path - Get configuration path for your system

Making Changes

Edit files in the src/ directory
Run npm run build to compile
Restart your MCP client to use the updated server

Testing

npm run test

This runs an OpenAI API connectivity test that verifies:

OpenAI API key is configured correctly
API connection is working
Available image generation models
GPT-Image-1 model access

API Costs

This server uses OpenAI's API, which charges per image generation. Check OpenAI pricing for current rates.

Typical costs (as of 2024):

GPT-Image-1: ~$0.04-0.08 per image depending on size and quality
HD quality images cost more than standard quality
Costs vary by resolution (1024x1024 vs 1536x1024)

Troubleshooting

Server not appearing in MCP client

Recommended: Use the npx configuration for universal compatibility
If using local installation, verify the path to build/index.js is correct and absolute
Ensure Node.js 18+ is installed: node --version
Test server startup: npm run test
Restart your MCP client (Claude Desktop, Kilo Code, etc.)
Note: Server will start successfully even without OPENAI_API_KEY - check tool responses for API key errors

Image generation failing

Verify your OpenAI API key is valid and has sufficient credits
Ensure you have access to the GPT-Image-1 model (may require specific API tier)
Check that your prompt follows OpenAI's content policy
Try reducing the number of images or simplifying the prompt
Check the server logs for detailed error messages
Verify your account has image generation capabilities enabled

Build issues

If you need to rebuild the server:

npm install
npm run build

Configuration issues

Use the helper script to get the correct path:

npm run get-path

Support

For issues with:

This MCP server: Create an issue in this repository
OpenAI API: Check OpenAI documentation
MCP Protocol: See MCP documentation

License

MIT License - see LICENSE file for details.

Contributing

Fork the repository
Create a feature branch
Make your changes
Test with npm run test
Submit a pull request

Changelog

v1.0.0 (Latest)

🎨 Initial release with GPT-Image-1: Complete rewrite to use OpenAI's GPT-Image-1 model
📥 Automatic base64 processing: Generated images automatically converted and saved as PNG files
🗂️ Smart filename generation: Images saved with descriptive names including prompt and timestamp
🔄 Enhanced tools: Two generation tools with different feature sets (basic and with variations)
📁 Auto-directory creation: Creates images folder automatically if it doesn't exist
🛡️ Robust error handling: Graceful handling of API errors and file save failures
🎨 Style support: Enhanced tool with style guidance and quality control
🌍 Universal portability: Updated for npx usage - works universally without local installation
✅ Production ready: Comprehensive error handling and user-friendly messages

Migration from FAL Imagen 4

If you're migrating from the previous FAL Imagen 4 MCP server:

Update your configuration to use OPENAI_API_KEY instead of FAL_KEY
Tool names changed:
- imagen4_generate → gpt_image_1_generate
- imagen4_generate_async → gpt_image_1_generate_with_variations
Parameter changes:
- aspect_ratio → size (with specific resolution options)
- negative_prompt → removed (not supported by GPT-Image-1)
- Added quality and style parameters for enhanced control
Response format: Now includes revised prompts and local PNG file paths
Image format: All images saved as PNG files instead of various formats

GPT-Image-1 MCP Server

🔗 Repository: https://github.com/PierrunoYT/gpt-image-1-mcp-server

🚀 Ready to use! Pre-built executable included - no compilation required.

✅ Enhanced Reliability: Server handles missing API keys gracefully without crashes and includes robust error handling.

Features

High-Quality Image Generation: Uses OpenAI's GPT-Image-1 model
Automatic Image Download: Generated images are automatically saved to local images directory as PNG files
Multiple Sizes: Support for 1024x1024, 1024x1536, and 1536x1024 resolutions
Batch Generation: Generate up to 4 images at once
Style Variations: Enhanced tool with style guidance support
Quality Control: Standard and HD quality options
Base64 Processing: Handles OpenAI's base64 image format automatically
Detailed Responses: Returns local file paths with generation metadata
Robust Error Handling: Graceful handling of missing API keys without server crashes
Universal Portability: Works anywhere with npx - no local installation required
Enhanced Reliability: Graceful shutdown handlers and comprehensive error reporting

Prerequisites

Node.js 18 or higher
OpenAI API key with access to GPT-Image-1 model

Installation

1. Get your OpenAI API Key

Visit OpenAI Platform
Sign up for an account or log in
Navigate to API Keys section
Generate an API key
Ensure you have access to the GPT-Image-1 model

2. Clone or Download

git clone https://github.com/PierrunoYT/gpt-image-1-mcp-server.git
cd gpt-image-1-mcp-server

3. Install Dependencies (Optional)

The server is pre-built, but if you want to modify it:

npm install
npm run build

Configuration

🚀 Recommended: Universal npx Configuration (Works Everywhere)

Best option for portability - works on any machine with Node.js:

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "npx",
      "args": [
        "-y",
        "https://github.com/PierrunoYT/gpt-image-1-mcp-server.git"
      ],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

Benefits:

✅ Universal Access: Works on any machine with Node.js
✅ No Local Installation: npx downloads and runs automatically
✅ Always Latest Version: Pulls from GitHub repository
✅ Cross-Platform: Windows, macOS, Linux compatible
✅ Settings Sync: Works everywhere you use your MCP client

Alternative: Local Installation

If you prefer to install locally, use the path helper:

npm run get-path

This will output the complete MCP configuration with the correct absolute path.

For Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "node",
      "args": ["path/to/gpt-image-1-mcp-server/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

For Kilo Code MCP Settings

Add to your MCP settings file at: C:\Users\[username]\AppData\Roaming\Kilo-Code\MCP\settings\mcp_settings.json

{
  "mcpServers": {
    "gpt-image-1": {
      "command": "node",
      "args": ["path/to/gpt-image-1-mcp-server/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Available Tools

`gpt_image_1_generate`

Generate images using OpenAI's GPT-Image-1 model with standard processing.

Parameters:

prompt (required): Text description of the image to generate
size (optional): "1024x1024", "1024x1536", or "1536x1024" (default: "1024x1024")
n (optional): Number of images to generate, 1-4 (default: 1)

Response includes:

Local file paths for saved PNG images
Generation metadata (revised prompts if applicable)
File information and save status

`gpt_image_1_generate_with_variations`

Generate images using GPT-Image-1 with enhanced style control and quality options.

Parameters:

prompt (required): Text description of the image to generate
size (optional): "1024x1024", "1024x1536", or "1536x1024" (default: "1024x1024")
n (optional): Number of images to generate, 1-4 (default: 1)
style (optional): Style guidance for the image generation
quality (optional): "standard" or "hd" (default: "standard")

Use this tool when:

You want to specify a particular artistic style
You need higher quality (HD) images
You want enhanced control over the generation process
Creating variations of a concept with different styles

Features:

Style-enhanced prompts for better artistic control
HD quality option for higher resolution details
Enhanced metadata in responses

📥 How Image Download Works

The GPT-Image-1 MCP server automatically processes and saves generated images to your local machine. Here's the complete process:

1. Image Generation Flow

API Call: Server calls OpenAI's GPT-Image-1 API
Response: OpenAI returns base64-encoded image data
Auto-Save: Server immediately converts and saves images as PNG files
Response: Returns local file paths and generation metadata

2. Base64 Processing Implementation

Save Function (`saveBase64Image`):

async function saveBase64Image(b64Data: string, filename: string): Promise<string> {
  // 1. Create 'images' directory if it doesn't exist
  const imagesDir = path.join(process.cwd(), 'images');
  if (!fs.existsSync(imagesDir)) {
    fs.mkdirSync(imagesDir, { recursive: true });
  }
  
  // 2. Convert base64 to buffer and save as PNG
  const filePath = path.join(imagesDir, filename);
  const buffer = Buffer.from(b64Data, 'base64');
  fs.writeFileSync(filePath, buffer);
  
  return filePath;
}

Filename Generation (`generateImageFilename`):

function generateImageFilename(prompt: string, index: number): string {
  // Creates safe filename: gpt_image_1_prompt_index_timestamp.png
  const safePrompt = prompt
    .toLowerCase()
    .replace(/[^a-z0-9\s]/g, '')  // Remove special characters
    .replace(/\s+/g, '_')         // Replace spaces with underscores
    .substring(0, 50);            // Limit length
  
  const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
  return `gpt_image_1_${safePrompt}_${index}_${timestamp}.png`;
}

3. File Storage Details

Directory Structure:

your-project/
├── images/                    # Auto-created directory
│   ├── gpt_image_1_mountain_landscape_1_2025-06-24T18-30-45-123Z.png
│   ├── gpt_image_1_cute_robot_1_2025-06-24T18-31-20-456Z.png
│   └── ...

Filename Format:

Prefix: gpt_image_1_
Prompt: First 50 chars, sanitized (alphanumeric + underscores)
Index: Image number (for multiple images)
Timestamp: ISO timestamp for uniqueness
Extension: .png

4. Response Format

The server returns detailed information about saved images:

Successfully generated 1 image(s) using GPT-Image-1:

Prompt: "a serene mountain landscape"
Size: 1024x1024
Number of Images: 1

Generated Images:
Image 1:
  Local Path: /path/to/project/images/gpt_image_1_a_serene_mountain_landscape_1_2025-06-24T18-30-45-123Z.png
  Revised Prompt: A serene mountain landscape with snow-capped peaks...

Images have been saved to the local 'images' directory.

5. Benefits of Local Processing

Example Usage

Basic Image Generation

Generate a photorealistic image of a golden retriever playing in a field of sunflowers

With Specific Parameters

Generate an image with:
- Prompt: "A minimalist logo design for a tech startup, clean lines"
- Size: 1536x1024
- Number of images: 2

Advanced Usage with Style

Generate an image of "A futuristic cityscape at night with neon lights and flying cars" 
with style "cyberpunk art" and HD quality

Technical Details

Architecture

Language: TypeScript with ES2022 target
Runtime: Node.js 18+ with ES modules
Protocol: Model Context Protocol (MCP) SDK v1.0.0
API Client: OpenAI JavaScript SDK v4.0.0
Validation: Zod schema validation

API Model Used

Model: gpt-image-1 (OpenAI's latest image generation model)
Response Format: b64_json (base64-encoded PNG data)
Supported Sizes: 1024x1024, 1024x1536, 1536x1024

Error Handling

Graceful API key handling: Server continues running even without OPENAI_API_KEY set
No crash failures: Removed process.exit() calls that caused connection drops
Null safety checks: All tools validate API client availability before execution
Graceful shutdown: Proper SIGINT and SIGTERM signal handling
API error catching: Comprehensive error reporting with detailed context
File save error handling: Graceful fallback with detailed error messages
User-friendly messages: Clear error descriptions instead of technical crashes

Development

Project Structure

├── src/
│   └── index.ts          # Main MCP server implementation
├── build/                # Compiled JavaScript (ready to use)
├── test-server.js        # Server testing utility
├── get-path.js          # Configuration path helper
├── example-mcp-config.json # Example configuration
├── package.json         # Project metadata and dependencies
└── tsconfig.json        # TypeScript configuration

Scripts

npm run build - Compile TypeScript to JavaScript
npm run dev - Watch mode for development
npm run start - Start the server directly
npm run test - Test server startup and basic functionality
npm run get-path - Get configuration path for your system

Making Changes

Edit files in the src/ directory
Run npm run build to compile
Restart your MCP client to use the updated server

Testing

npm run test

This runs an OpenAI API connectivity test that verifies:

OpenAI API key is configured correctly
API connection is working
Available image generation models
GPT-Image-1 model access

API Costs

This server uses OpenAI's API, which charges per image generation. Check OpenAI pricing for current rates.

Typical costs (as of 2024):

GPT-Image-1: ~$0.04-0.08 per image depending on size and quality
HD quality images cost more than standard quality
Costs vary by resolution (1024x1024 vs 1536x1024)

Troubleshooting

Server not appearing in MCP client

Recommended: Use the npx configuration for universal compatibility
If using local installation, verify the path to build/index.js is correct and absolute
Ensure Node.js 18+ is installed: node --version
Test server startup: npm run test
Restart your MCP client (Claude Desktop, Kilo Code, etc.)
Note: Server will start successfully even without OPENAI_API_KEY - check tool responses for API key errors

Image generation failing

Verify your OpenAI API key is valid and has sufficient credits
Ensure you have access to the GPT-Image-1 model (may require specific API tier)
Check that your prompt follows OpenAI's content policy
Try reducing the number of images or simplifying the prompt
Check the server logs for detailed error messages
Verify your account has image generation capabilities enabled

Build issues

If you need to rebuild the server:

npm install
npm run build

Configuration issues

Use the helper script to get the correct path:

npm run get-path

Support

For issues with:

This MCP server: Create an issue in this repository
OpenAI API: Check OpenAI documentation
MCP Protocol: See MCP documentation

License

MIT License - see LICENSE file for details.

Contributing

Fork the repository
Create a feature branch
Make your changes
Test with npm run test
Submit a pull request

Changelog

v1.0.0 (Latest)

🎨 Initial release with GPT-Image-1: Complete rewrite to use OpenAI's GPT-Image-1 model
📥 Automatic base64 processing: Generated images automatically converted and saved as PNG files
🗂️ Smart filename generation: Images saved with descriptive names including prompt and timestamp
🔄 Enhanced tools: Two generation tools with different feature sets (basic and with variations)
📁 Auto-directory creation: Creates images folder automatically if it doesn't exist
🛡️ Robust error handling: Graceful handling of API errors and file save failures
🎨 Style support: Enhanced tool with style guidance and quality control
🌍 Universal portability: Updated for npx usage - works universally without local installation
✅ Production ready: Comprehensive error handling and user-friendly messages

Migration from FAL Imagen 4

If you're migrating from the previous FAL Imagen 4 MCP server:

Update your configuration to use OPENAI_API_KEY instead of FAL_KEY
Tool names changed:
- imagen4_generate → gpt_image_1_generate
- imagen4_generate_async → gpt_image_1_generate_with_variations
Parameter changes:
- aspect_ratio → size (with specific resolution options)
- negative_prompt → removed (not supported by GPT-Image-1)
- Added quality and style parameters for enhanced control
Response format: Now includes revised prompts and local PNG file paths
Image format: All images saved as PNG files instead of various formats

GPT-Image-1 MCP Server

GPT-Image-1 MCP Server

Features

Prerequisites

Installation

1. Get your OpenAI API Key

2. Clone or Download

3. Install Dependencies (Optional)

Configuration

🚀 Recommended: Universal npx Configuration (Works Everywhere)

Alternative: Local Installation

For Claude Desktop

For Kilo Code MCP Settings

Available Tools

gpt_image_1_generate

gpt_image_1_generate_with_variations

📥 How Image Download Works

1. Image Generation Flow

2. Base64 Processing Implementation

Save Function (saveBase64Image):

Filename Generation (generateImageFilename):

3. File Storage Details

Directory Structure:

Filename Format:

4. Response Format

5. Benefits of Local Processing

Example Usage

Basic Image Generation

With Specific Parameters

Advanced Usage with Style

Technical Details

Architecture

API Model Used

Error Handling

Development

Project Structure

Scripts

Making Changes

Testing

API Costs

Troubleshooting

Server not appearing in MCP client

Image generation failing

Build issues

Configuration issues

Support

License

Contributing

Changelog

v1.0.0 (Latest)

Migration from FAL Imagen 4

GPT-Image-1 MCP Server

Features

Prerequisites

Installation

1. Get your OpenAI API Key

2. Clone or Download

3. Install Dependencies (Optional)

Configuration

🚀 Recommended: Universal npx Configuration (Works Everywhere)

Alternative: Local Installation

For Claude Desktop

For Kilo Code MCP Settings

Available Tools

gpt_image_1_generate

gpt_image_1_generate_with_variations

📥 How Image Download Works

1. Image Generation Flow

2. Base64 Processing Implementation

Save Function (saveBase64Image):

Filename Generation (generateImageFilename):

3. File Storage Details

Directory Structure:

Filename Format:

4. Response Format

5. Benefits of Local Processing

Example Usage

Basic Image Generation

With Specific Parameters

Advanced Usage with Style

`gpt_image_1_generate`

`gpt_image_1_generate_with_variations`

Save Function (`saveBase64Image`):

Filename Generation (`generateImageFilename`):

`gpt_image_1_generate`

`gpt_image_1_generate_with_variations`

Save Function (`saveBase64Image`):

Filename Generation (`generateImageFilename`):