openai-gpt-image-mcp
A Model Context Protocol (MCP) tool server for OpenAI's GPT-4o/gpt-image-1 image generation and editing APIs.
- • Basic MCP protocol features implemented (12/40)
- • Limited GitHub community activity (6/20)
- • Optimal dependency management (20/20)
- • Room for improvement in deployment maturity
- • Documentation (8/8)
- • Archestra MCP Trust score badge is missing
{
"openai-gpt-image-mcp": {
"command": "node",
"args": [
"dist/index.js"
],
"env": {}
},
"openai-gpt-image-mcp-configured": {
"command": "node",
"args": [
"/absolute/path/to/dist/index.js"
],
"env": {
"OPENAI_API_KEY": "sk-..."
}
},
"openai-gpt-image-mcp-azure": {
"command": "node",
"args": [
"/absolute/path/to/dist/index.js"
],
"env": {
"AZURE_OPENAI_API_KEY": "sk-...",
"AZURE_OPENAI_ENDPOINT": "my.endpoint.com",
"OPENAI_API_VERSION": "2024-12-01-preview"
}
},
"openai-gpt-image-mcp-with-env-file": {
"command": "node",
"args": [
"/absolute/path/to/dist/index.js",
"--env-file",
"./deployment/.env"
],
"env": {}
}
}openai-gpt-image-mcp
A Model Context Protocol (MCP) tool server for OpenAI's GPT-4o/gpt-image-1 image generation and editing APIs.
- Generate images from text prompts using OpenAI's latest models.
- Edit images (inpainting, outpainting, compositing) with advanced prompt control.
- Supports: Claude Desktop, Cursor, VSCode, Windsurf, and any MCP-compatible client.
✨ Features
- create-image: Generate images from a prompt, with advanced options (size, quality, background, etc).
- edit-image: Edit or extend images using a prompt and optional mask, supporting both file paths and base64 input.
- File output: Save generated images directly to disk, or receive as base64.
🚀 Installation
git clone https://github.com/SureScaleAI/openai-gpt-image-mcp.git
cd openai-gpt-image-mcp
yarn install
yarn build
🔑 Configuration
Add to Claude Desktop or VSCode (including Cursor/Windsurf) config:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": { "OPENAI_API_KEY": "sk-..." }
}
}
}
Also supports Azure deployments:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": {
"AZURE_OPENAI_API_KEY": "sk-...",
"AZURE_OPENAI_ENDPOINT": "my.endpoint.com",
"OPENAI_API_VERSION": "2024-12-01-preview"
}
}
}
}
Also supports supplying an environment files:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js", "--env-file", "./deployment/.env"]
}
}
}
⚡ Advanced
- For
create-image, setnto generate up to 10 images at once. - For
edit-image, provide a mask image (file path or base64) to control where edits are applied. - Provide an environment file with
--env-file path/to/file/.env - See
src/index.tsfor all options.
🧑💻 Development
- TypeScript source:
src/index.ts - Build:
yarn build - Run:
node dist/index.js
📝 License
MIT
🩺 Troubleshooting
- Make sure your
OPENAI_API_KEYis valid and has image API access. - You must have a verified OpenAI organization. After verifying, it can take 15–20 minutes for image API access to activate.
- File paths must be absolute.
- Unix/macOS/Linux: Starting with
/(e.g.,/path/to/image.png) - Windows: Drive letter followed by
:(e.g.,C:/path/to/image.pngorC:\path\to\image.png)
- Unix/macOS/Linux: Starting with
- For file output, ensure the directory is writable.
- If you see errors about file types, check your image file extensions and formats.
⚠️ Limitations & Large File Handling
- 1MB Payload Limit: MCP clients (including Claude Desktop) have a hard 1MB limit for tool responses. Large images (especially high-res or multiple images) can easily exceed this limit if returned as base64.
- Auto-Switch to File Output: If the total image size exceeds 1MB, the tool will automatically save images to disk and return the file path(s) instead of base64. This ensures compatibility and prevents errors like
result exceeds maximum length of 1048576. - Default File Location: If you do not specify a
file_outputpath, images will be saved to/tmp(or the directory set by theMCP_HF_WORK_DIRenvironment variable) with a unique filename. - Environment Variable:
MCP_HF_WORK_DIR: Set this to control where large images and file outputs are saved. Example:export MCP_HF_WORK_DIR=/your/desired/dir
- Best Practice: For large or production images, always use file output and ensure your client is configured to handle file paths.
📚 References
🙏 Credits
- Built with @modelcontextprotocol/sdk
- Uses openai Node.js SDK
- Built by SureScale.ai
- Contributions from Axle Research and Technology
[](https://archestra.ai/mcp-catalog/surescaleai__openai-gpt-image-mcp)openai-gpt-image-mcp
A Model Context Protocol (MCP) tool server for OpenAI's GPT-4o/gpt-image-1 image generation and editing APIs.
- Generate images from text prompts using OpenAI's latest models.
- Edit images (inpainting, outpainting, compositing) with advanced prompt control.
- Supports: Claude Desktop, Cursor, VSCode, Windsurf, and any MCP-compatible client.
✨ Features
- create-image: Generate images from a prompt, with advanced options (size, quality, background, etc).
- edit-image: Edit or extend images using a prompt and optional mask, supporting both file paths and base64 input.
- File output: Save generated images directly to disk, or receive as base64.
🚀 Installation
git clone https://github.com/SureScaleAI/openai-gpt-image-mcp.git
cd openai-gpt-image-mcp
yarn install
yarn build
🔑 Configuration
Add to Claude Desktop or VSCode (including Cursor/Windsurf) config:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": { "OPENAI_API_KEY": "sk-..." }
}
}
}
Also supports Azure deployments:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": {
"AZURE_OPENAI_API_KEY": "sk-...",
"AZURE_OPENAI_ENDPOINT": "my.endpoint.com",
"OPENAI_API_VERSION": "2024-12-01-preview"
}
}
}
}
Also supports supplying an environment files:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js", "--env-file", "./deployment/.env"]
}
}
}
⚡ Advanced
- For
create-image, setnto generate up to 10 images at once. - For
edit-image, provide a mask image (file path or base64) to control where edits are applied. - Provide an environment file with
--env-file path/to/file/.env - See
src/index.tsfor all options.
🧑💻 Development
- TypeScript source:
src/index.ts - Build:
yarn build - Run:
node dist/index.js
📝 License
MIT
🩺 Troubleshooting
- Make sure your
OPENAI_API_KEYis valid and has image API access. - You must have a verified OpenAI organization. After verifying, it can take 15–20 minutes for image API access to activate.
- File paths must be absolute.
- Unix/macOS/Linux: Starting with
/(e.g.,/path/to/image.png) - Windows: Drive letter followed by
:(e.g.,C:/path/to/image.pngorC:\path\to\image.png)
- Unix/macOS/Linux: Starting with
- For file output, ensure the directory is writable.
- If you see errors about file types, check your image file extensions and formats.
⚠️ Limitations & Large File Handling
- 1MB Payload Limit: MCP clients (including Claude Desktop) have a hard 1MB limit for tool responses. Large images (especially high-res or multiple images) can easily exceed this limit if returned as base64.
- Auto-Switch to File Output: If the total image size exceeds 1MB, the tool will automatically save images to disk and return the file path(s) instead of base64. This ensures compatibility and prevents errors like
result exceeds maximum length of 1048576. - Default File Location: If you do not specify a
file_outputpath, images will be saved to/tmp(or the directory set by theMCP_HF_WORK_DIRenvironment variable) with a unique filename. - Environment Variable:
MCP_HF_WORK_DIR: Set this to control where large images and file outputs are saved. Example:export MCP_HF_WORK_DIR=/your/desired/dir
- Best Practice: For large or production images, always use file output and ensure your client is configured to handle file paths.
📚 References
🙏 Credits
- Built with @modelcontextprotocol/sdk
- Uses openai Node.js SDK
- Built by SureScale.ai
- Contributions from Axle Research and Technology
Related MCP Servers
mcp-shrimp-task-manager
88/100Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.
cognee-mcp
86/100Memory for AI Agents in 5 lines of code
shadcn-ui-mcp-server
82/100A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation,compaitable with react,svelte 5,and vue
mobile-mcp
81/100Model Context Protocol Server for Mobile Automation and Scraping (iOS, Android, Emulators, Simulators and Real Devices)
HuggingFace
80/100Remote MCP server for HuggingFace services - access models, datasets, and spaces
markitdown-mcp
80/100Python tool for converting files and office documents to Markdown.
