OpenAI GPT-Image MCP Server

English | 日本語

A Model Context Protocol (MCP) server that enables image generation and editing using OpenAI's GPT Image API (gpt-image-1, gpt-image-1.5, and gpt-image-2). Works seamlessly with Claude Desktop, Claude Code, and other MCP-compatible clients.

Features

Core Capabilities

• 🎨 High-Quality Image Generation: State-of-the-art text-to-image generation

• 📝 Excellent Text Rendering: Accurate text rendering within images

• ✂️ Precise Image Editing: Inpainting for targeted modifications

• 🔄 Image Transformation: Style transfer and reinterpretation

• 📐 Flexible Sizing: Square, portrait, and landscape formats

• 🎚️ Quality Control: Choose from low, medium, or high quality

• 🖼️ Multiple Formats: PNG, JPEG, and WebP support

• 🌐 Cross-Platform: Works on macOS, Windows, and Linux with smart path handling

Advanced Features (v1.0.3+)

• 🎲 Multi-Image Generation: Generate 1-10 images in a single request

• 📚 History Management: SQLite-based generation history with search

• ⚡ Async Job System: Background processing with progress tracking

• 🏷️ Metadata Embedding: Automatic metadata in PNG/JPEG files

• 💰 Cost Management: Automatic token usage and cost estimation

• 🛡️ Content Filtering: Built-in safety filters

• 📁 Image Management: List and organize generated images

• 🔧 Debug Mode: Detailed logging for troubleshooting

GPT-Image-1.5 Support (v1.2.0+)

• 🚀 gpt-image-1.5 Model: 4x faster generation, 20% lower cost

• 📝 Better Text Rendering: Improved accuracy for text in images

• 👤 Input Fidelity: High-fidelity face/logo preservation (edit & transform)

• 🔧 Model Selection: Choose between gpt-image-1 and gpt-image-1.5

GPT-Image-2 Support (v1.3.0+)

• 🆕 gpt-image-2 Model: Latest OpenAI image model with flexible sizing

• 📐 Flexible Sizes: 2K/4K presets (2048x2048, 2048x1152, 3840x2160, etc.) and custom WxH (16px multiples, each edge ≤3840, ratio ≤3:1, 0.65–8.3 megapixels)

• 🧪 Experimental 4K: 3840x2160 / 2160x3840 available (quality/stability not guaranteed by OpenAI)

• 🎯 Auto High-Fidelity: Reference images are always processed at high fidelity — input_fidelity is ignored

• ⚠️ No Transparent Background: transparent_background is not supported; use gpt-image-1 / 1.5 if you need transparency

• 💵 Lower Low-Tier Pricing: low quality 1024x1024 is ~$0.006/image

Batch Processing (v1.1.0+)

• 📦 CLI Batch Tool: Generate multiple images at once via command line

• 🔄 Concurrency Control: Parallel processing with configurable limits

• 📊 Cost Estimation: Preview costs before execution

• ⚙️ Retry Policy: Automatic retry for failed jobs

• 📝 Multiple Output Formats: Text or JSON results

• 🤖 GitHub Actions: Automated batch generation from Issue comments

• 💾 Batch History: Track and manage batch executions

Related MCP server: MCP OpenAI Image Generation Server

Prerequisites

• Node.js v18 or higher

• OpenAI API Key with verified organization

• MCP-compatible client (Claude Desktop, Claude Code, etc.)

⚠️ Important: Using gpt-image-1 requires OpenAI Organization Verification.

Quick Start (5 Minutes)

Prerequisites: Node.js 18+, OpenAI API key with verified organization

1. Install

npm install -g openai-gpt-image-mcp-server

2. Configure

Add to your Claude Desktop config file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "openai-gpt-image": {
      "command": "openai-gpt-image-mcp-server",
      "env": {
        "OPENAI_API_KEY": "sk-proj-your-key-here"
      }
    }
  }
}

Windows users: Use openai-gpt-image-mcp-server.cmd as the command.

3. Restart Claude Desktop

Completely restart Claude Desktop (quit from system tray/menu bar).

4. Test

In Claude, try: "Generate a beautiful sunset landscape"

Done! For detailed setup and advanced features, see Full Installation Guide below.

Installation

Quick Install

npm install -g openai-gpt-image-mcp-server

From Source

git clone https://github.com/ex-takashima/openAI-gpt-image-1-MCP-SERVER.git
cd openAI-gpt-image-1-MCP-SERVER
npm install
npm run build

Setup

1. Get Your OpenAI API Key

1. Visit OpenAI Platform

2. Log in or create an account

3. Complete Organization Verification:

   • Go to Settings > Organization > General

   • Click "Verify Organization"

   • Upload government-issued ID

   • Complete facial verification

   • Wait up to 15 minutes for approval

4. Create a new API key in the API Keys section

5. Save the key securely

2. Configure API Key

Set your API key as an environment variable:

# Linux/macOS
export OPENAI_API_KEY="sk-proj-..."

# Windows (PowerShell)
$env:OPENAI_API_KEY="sk-proj-..."

Or create a .env file:

OPENAI_API_KEY=sk-proj-your-api-key-here

3. Configure Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "openai-gpt-image": {
      "command": "openai-gpt-image-mcp-server",
      "env": {
        "OPENAI_API_KEY": "sk-proj-your-api-key-here",
        "OPENAI_IMAGE_OUTPUT_DIR": "/Users/username/Pictures/ai-images"
      }
    }
  }
}

Windows users: Use openai-gpt-image-mcp-server.cmd as the command.

Optional Environment Variables:

• OPENAI_IMAGE_OUTPUT_DIR: Custom output directory (default: ~/Downloads/openai-images)

• OPENAI_IMAGE_INPUT_DIR: Custom input directory (default: same as output directory)

• OPENAI_IMAGE_EMBED_METADATA: Enable metadata embedding (true/false, default: true)

• OPENAI_IMAGE_METADATA_LEVEL: Metadata detail level (minimal/standard/full, default: standard)

• OPENAI_IMAGE_THUMBNAIL: Enable thumbnail generation (true/false, default: false)

• OPENAI_IMAGE_THUMBNAIL_SIZE: Thumbnail size in pixels (default: 128, range: 1-512)

• OPENAI_IMAGE_THUMBNAIL_QUALITY: Thumbnail JPEG quality (default: 60, range: 1-100)

• OPENAI_ORGANIZATION: OpenAI organization ID (if you belong to multiple)

• HISTORY_DB_PATH: Custom database location (default: ~/.openai-gpt-image/history.db)

• DEBUG: Set to 1 for detailed logging

📖 Complete reference: See Environment Variables Reference for detailed documentation of all variables.

Restart Claude Desktop after saving.

4. Configure Claude Code

For Claude Code, use this configuration:

Windows:

{
  "mcpServers": {
    "openai-gpt-image": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "openai-gpt-image-mcp-server"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-your-api-key-here"
      }
    }
  }
}

macOS/Linux:

{
  "mcpServers": {
    "openai-gpt-image": {
      "command": "npx",
      "args": ["-y", "openai-gpt-image-mcp-server"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-your-api-key-here"
      }
    }
  }
}

Usage Examples

Basic Image Generation

Generate a beautiful sunset landscape

With Size Specification

Generate a 1536x1024 wide mountain landscape

With Quality

Generate a high-quality image of an astronaut floating in space

Text Rendering

Create an image with "WELCOME" written on a large sign

Image Editing

Edit this photo's background. Use the mask image to change only
the background to a beautiful beach.

Image Transformation

Transform this photo into an oil painting style

Transparent Background

Generate an illustration of an apple with a transparent background

Multi-Image Generation (v1.0.3+)

Generate 5 different variations of a cyberpunk cityscape

View History (v1.0.3+)

Show me my image generation history from the last week

Async Jobs (v1.0.3+)

Start a background job to generate 10 high-quality landscape images.
I want to continue working while it processes.

Batch Processing

Generate multiple images at once using the CLI batch tool.

Quick Start

# Basic batch generation
openai-gpt-image-batch examples/batch-simple.json

# Estimate cost before execution
openai-gpt-image-batch examples/batch-detailed.json --estimate-only

# JSON output format
openai-gpt-image-batch examples/batch-large-scale.json --format json > result.json

Batch Configuration Example

{
  "jobs": [
    {
      "prompt": "A beautiful sunset over the ocean",
      "output_path": "sunset.png",
      "size": "1536x1024",
      "quality": "high"
    },
    {
      "prompt": "A futuristic city skyline",
      "output_path": "city.png",
      "quality": "medium"
    }
  ],
  "max_concurrent": 3,
  "timeout": 900000
}

CLI Options

openai-gpt-image-batch <config.json> [options]

Options:
  --output-dir <path>      Output directory
  --format <text|json>     Output format (default: text)
  --timeout <ms>           Timeout in milliseconds
  --max-concurrent <n>     Max concurrent jobs (1-10)
  --estimate-only          Estimate cost without executing
  --help, -h               Show help
  --version, -v            Show version

Features

• Concurrency Control: Parallel processing with configurable limits (1-10 concurrent jobs)

• Cost Estimation: Preview costs before execution with --estimate-only

• Retry Policy: Automatic retry for failed jobs (configurable)

• Multiple Output Formats: Results in text or JSON format

• Timeout Management: Prevent long-running executions

• Error Handling: Continue processing even if individual jobs fail

• GitHub Actions Integration: Automated batch generation from Issue comments

Sample Configurations

Four example configurations are included:

1. batch-simple.json: Basic batch with 3 images

2. batch-detailed.json: Detailed configuration with custom settings (5 images)

3. batch-multi-variant.json: Multi-variant generation (3-5 variants per prompt)

4. batch-large-scale.json: Large-scale batch processing (10+ images)

Documentation

For detailed documentation, see:

• docs/BATCH_PROCESSING.md - English

• docs/BATCH_PROCESSING.ja.md - 日本語

Documentation includes:

• Comprehensive CLI usage guide

• Batch configuration JSON format

• GitHub Actions integration

• Troubleshooting guide

• Best practices

Available Tools

1. generate_image

Generate new images from text prompts.

Parameters:

• prompt (required): Image description

• output_path: Save location (default: generated_image.png)

• size: 1024x1024, 1024x1536, 1536x1024, or auto

• quality: low, medium, high, or auto

• output_format: png, jpeg, or webp

• transparent_background: Enable transparency (PNG only)

• moderation: Content filtering level

• sample_count: Number of images to generate (1-10, default: 1)

• return_base64: Return base64-encoded image

2. edit_image

Edit images using inpainting.

Parameters:

• prompt (required): Edit description

• reference_image_base64 or reference_image_path: Source image

• mask_image_base64 or mask_image_path: Mask (transparent = edit area)

• output_path: Save location

• sample_count: Number of images to generate (1-10, default: 1)

• Other parameters same as generate_image

3. transform_image

Transform images to new styles.

Parameters:

• prompt (required): Transformation description

• reference_image_base64 or reference_image_path: Source image

• output_path: Save location

• sample_count: Number of images to generate (1-10, default: 1)

• Other parameters same as generate_image

4. list_generated_images

List images in a directory.

Parameters:

• directory: Path to search (default: current directory)

5. list_history

Browse generation history with optional filters.

Parameters:

• limit: Max records (1-100, default: 20)

• offset: Skip N records (pagination)

• tool_name: Filter by tool (generate_image, edit_image, transform_image)

• query: Search in prompts

6. get_history_by_uuid

Get detailed information about a specific generation.

Parameters:

• uuid (required): History record UUID

7. start_generation_job

Start an async image generation job in the background.

Parameters:

• tool_name (required): Which tool to use

• prompt (required): Generation prompt

• Other parameters same as the respective tool

8. check_job_status

Check the status of an async job.

Parameters:

• job_id (required): Job ID from start_generation_job

9. get_job_result

Get the result of a completed job.

Parameters:

• job_id (required): Job ID

10. cancel_job

Cancel a pending or running job.

Parameters:

• job_id (required): Job ID to cancel

11. list_jobs

List async jobs with optional filters.

Parameters:

• status: Filter by status (pending, running, completed, failed, cancelled)

• tool_name: Filter by tool

• limit: Max results (1-100, default: 20)

• offset: Skip N results

Advanced Features

Multi-Image Generation

All generation tools support the sample_count parameter to generate multiple images at once:

Generate 5 variations of a cat playing with yarn

• Supported range: 1-10 images per request

• Files are automatically numbered: output_1.png, output_2.png, etc.

• Cost is multiplied by the number of images

• All files are recorded in history

History Management

Every generation is automatically saved to a local SQLite database (~/.openai-gpt-image/history.db):

View recent history:

Show me the last 10 images I generated

Search history:

Find all images I generated with "sunset" in the prompt

Get details:

Show me the details for this history ID: 8796265a-8dc8-48f4-9b40-fe241985379b

The history includes:

• Generation timestamp

• Tool used

• Prompt and parameters

• Output file paths

• Cost information

Async Job System

For long-running operations or batch processing, use async jobs:

Start a background job:

Start a background job to generate 10 high-quality space images

Check status:

Check the status of job b7912655-0d8e-4ecc-be58-cbc2c4746932

Get results:

Get the results for job b7912655-0d8e-4ecc-be58-cbc2c4746932

Job statuses:

• ⏳ pending: Waiting to start

• 🔄 running: Currently processing

• ✅ completed: Finished successfully

• ❌ failed: Error occurred

• 🚫 cancelled: Manually cancelled

Metadata Embedding

Generated images automatically include embedded metadata:

PNG files: tEXt chunks with:

• openai_gpt_image_uuid: Unique identifier

• params_hash: SHA-256 hash of parameters

• tool_name: Tool used (generate_image, edit_image, transform_image)

• model: Model name (gpt-image-1)

• created_at: ISO 8601 timestamp

• size: Image dimensions (e.g., "1024x1024")

• quality: Quality level (low, medium, high)

• prompt: Generation prompt (full level only)

• parameters: Complete generation parameters (full level only)

JPEG/WebP files: EXIF ImageDescription with JSON metadata

View metadata:

# macOS/Linux
exiftool generated_image.png | grep openai

# Windows (PowerShell)
exiftool generated_image.png

This allows you to identify how an image was created even after moving it to different locations.

Controlling Metadata Embedding

You can control metadata embedding behavior using environment variables:

Disable metadata embedding entirely:

{
  "mcpServers": {
    "openai-gpt-image": {
      "env": {
        "OPENAI_API_KEY": "sk-proj-...",
        "OPENAI_IMAGE_EMBED_METADATA": "false"
      }
    }
  }
}

Change metadata detail level:

{
  "mcpServers": {
    "openai-gpt-image": {
      "env": {
        "OPENAI_API_KEY": "sk-proj-...",
        "OPENAI_IMAGE_METADATA_LEVEL": "minimal"
      }
    }
  }
}

Metadata levels:

• minimal: UUID and parameter hash only

  • Best for: Privacy-focused use cases

  • Size impact: Minimal (~100 bytes)

  • Contains: openai_gpt_image_uuid, params_hash

• standard (default): Basic generation information

  • Best for: Most use cases, balances detail and privacy

  • Size impact: Small (~300 bytes)

  • Contains: All minimal fields + tool_name, model, created_at, size, quality

• full: Complete generation details

  • Best for: Full traceability and reproducibility

  • Size impact: Medium (varies by prompt length, typically 500-2000 bytes)

  • Contains: All standard fields + prompt, parameters

Note: Metadata embedding is "best effort" - if embedding fails, the image is still saved without metadata. Enable DEBUG=1 to see metadata embedding details.

Output Path Handling

Images are saved with smart cross-platform path handling:

Default Behavior

By default, all images are saved to ~/Downloads/openai-images:

• macOS: /Users/username/Downloads/openai-images/

• Windows: C:\Users\username\Downloads\openai-images\

• Linux: /home/username/Downloads/openai-images/

Path Resolution Priority

1. Absolute paths: Must be within base directory (security sandboxing)

   ~/Downloads/openai-images/myimage.png → ✅ saved (within base)
   /tmp/myimage.png → ❌ rejected (outside base)

2. Relative paths: Resolved from base directory

   myimage.png → ~/Downloads/openai-images/myimage.png
   subfolder/image.png → ~/Downloads/openai-images/subfolder/image.png

3. Security: Path traversal attacks prevented

   ../other/image.png → ❌ rejected (path traversal)

4. Auto-creation: Parent directories are created automatically

Custom Output Directory

Set the OPENAI_IMAGE_OUTPUT_DIR environment variable:

{
  "mcpServers": {
    "openai-gpt-image": {
      "env": {
        "OPENAI_API_KEY": "sk-proj-...",
        "OPENAI_IMAGE_OUTPUT_DIR": "/Users/username/Pictures/ai-images"
      }
    }
  }
}

Now myimage.png will be saved to /Users/username/Pictures/ai-images/myimage.png.

Input Path Handling

Input images (for edit_image and transform_image) are also managed with security:

Default Behavior

• Input directory: Same as output directory by default

• Can be customized with OPENAI_IMAGE_INPUT_DIR environment variable

Path Resolution

1. Relative paths: Resolved from input base directory

   photo.png → ~/Downloads/openai-images/photo.png
   source/photo.png → ~/Downloads/openai-images/source/photo.png

2. Absolute paths: Must be within base directory

   ~/Downloads/openai-images/photo.png → ✅ allowed
   /tmp/photo.png → ❌ rejected (outside base)

3. Security: Same sandboxing as output paths

   • Path traversal prevented

   • System files protected

   • Other user files protected

Separate Input/Output Directories

{
  "mcpServers": {
    "openai-gpt-image": {
      "env": {
        "OPENAI_API_KEY": "sk-proj-...",
        "OPENAI_IMAGE_INPUT_DIR": "~/Pictures/source-images",
        "OPENAI_IMAGE_OUTPUT_DIR": "~/Pictures/generated-images"
      }
    }
  }
}

Cost Management

All operations automatically report:

• Input/output token counts

• Estimated cost in USD

• Cost breakdown (text processing + image generation)

• Parameter details

Example output:

📊 Usage Statistics

- Input tokens: 15
- Output tokens (image): 4,096
- Total tokens: 4,111
- Estimated cost: $0.042

💰 Cost breakdown:
  - Text processing: $0.000150
  - Image generation: $0.041850

📏 Parameters: high quality | 1024x1024 | png

Pricing Examples

The following costs are approximate estimates. Actual pricing may vary.

Important: See OpenAI Pricing for current official rates.

Troubleshooting

Debug Mode

Enable detailed logging:

DEBUG=1 openai-gpt-image-mcp-server

Security

API Key Security

• Never commit API keys to version control

• Use environment variables or .env files

• Set file permissions: chmod 600 .env

• Rotate keys regularly

• Monitor usage at OpenAI Dashboard

File Access Sandboxing

All file operations (read/write) are restricted to configured base directories:

Protected system files:

• Unix/Linux/macOS: /etc/*, /var/*, /home/other_user/*, /root/*

• Windows: C:\Windows\*, C:\Program Files\*, C:\Users\OtherUser\*

Security features:

• ✅ Path traversal attack prevention (../ restrictions)

• ✅ System file protection

• ✅ Other user data protection

• ✅ Operations limited to configured directories only

To access different directories, configure base directories:

{
  "mcpServers": {
    "openai-gpt-image": {
      "env": {
        "OPENAI_IMAGE_OUTPUT_DIR": "/path/to/your/output",
        "OPENAI_IMAGE_INPUT_DIR": "/path/to/your/input"
      }
    }
  }
}

Development

# Install dependencies
npm install

# Build
npm run build

# Watch mode
npm run dev

# Run locally
npm start

Contributing

Contributions welcome! Please feel free to submit issues and pull requests.

License

MIT License - see LICENSE file for details.

Acknowledgments

• Model Context Protocol

• OpenAI API

• Claude Desktop

Happy Image Generating! 🎨