@cloudwerxlab/gpt-image-1-mcp
LocalAbout @cloudwerxlab/gpt-image-1-mcp
@cloudwerxlab/gpt-image-1-mcp is an MCP server for creating and editing images with OpenAI's gpt-image-1. It saves image files locally and returns paths and usage metadata.
Key features
- 01Generate images from text prompts
- 02Edit images with prompts and optional masks
- 03Accept image file paths for editing
- 04Save outputs to a configurable directory
- 05Return image paths and token usage
Use cases
- 01Create images from descriptions in an MCP client
- 02Revise an image using a prompt and mask
- 03Save generated images to a chosen folder
MCP support
Exposes create_image and create_image_edit tools to MCP clients. Requires an OpenAI API key with access to gpt-image-1.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
@cloudwerxlab/gpt-image-1-mcp
A Model Context Protocol (MCP) server for generating and editing images using the OpenAI gpt-image-1 model.
🚀 Quick Start
Run this MCP server directly using NPX without installing it. View on npm.
npx -y @cloudwerxlab/gpt-image-1-mcp
The -y flag automatically answers "yes" to any prompts that might appear during the installation process.
📋 Prerequisites
|
Node.js (v14 or higher) |
OpenAI API key with access to gpt-image-1 |
🔑 Environment Variables
| Variable | Required | Description |
|---|---|---|
OPENAI_API_KEY | ✅ Yes | Your OpenAI API key with access to the gpt-image-1 model |
GPT_IMAGE_OUTPUT_DIR | ❌ No | Custom directory for saving generated images (defaults to user's Pictures folder under gpt-image-1 subfolder) |
💻 Example Usage with NPX
| Operating System | Command Line Example |
|---|---|
| Linux/macOS |
|
| Windows (PowerShell) |
|
| Windows (Command Prompt) |
|
🔌 Integration with MCP Clients
🛠️ Setting Up in an MCP Client
Step 1: Locate Settings File
|
Step 2: Add ConfigurationAdd the following configuration to the |
{
"mcpServers": {
"gpt-image-1": {
"command": "npx",
"args": [
"-y",
"@cloudwerxlab/gpt-image-1-mcp"
],
"env": {
"OPENAI_API_KEY": "PASTE YOUR OPEN-AI KEY HERE",
"GPT_IMAGE_OUTPUT_DIR": "OPTIONAL: PATH TO SAVE GENERATED IMAGES"
}
}
}
}
Example Configurations for Different Operating Systems
| Operating System | Example Configuration |
|---|---|
| Windows |
|
| Linux/macOS |
|
Note: For Windows paths, use double backslashes (
\\) to escape the backslash character in JSON. For Linux/macOS, use forward slashes (/).
✨ Features
🎨 Core Tools
|
🚀 Key Benefits
|
💡 Enhanced Capabilities
📊 Output & Formatting
|
⚙️ Configuration & Handling
|
🔄 How It Works
| 🖼️ Image Generation | ✏️ Image Editing |
|---|---|
|
|
📁 Output Directory Behavior
📂 Storage Location
|
🗂️ File Management
|
Installation & Usage
NPM Package
This package is available on npm: @cloudwerxlab/gpt-image-1-mcp
You can install it globally:
npm install -g @cloudwerxlab/gpt-image-1-mcp
Or run it directly with npx as shown in the Quick Start section.
Tool: create_image
Generates a new image based on a text prompt.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
prompt | string | Yes | The text description of the image to generate (max 32,000 chars) |
size | string | No | Image size: "1024x1024" (default), "1536x1024", or "1024x1536" |
quality | string | No | Image quality: "high" (default), "medium", or "low" |
n | integer | No | Number of images to generate (1-10, default: 1) |
background | string | No | Background style: "transparent", "opaque", or "auto" (default) |
output_format | string | No | Output format: "png" (default), "jpeg", or "webp" |
output_compression | integer | No | Compression level (0-100, default: 0) |
user | string | No | User identifier for OpenAI usage tracking |
moderation | string | No | Moderation level: "low" or "auto" (default) |
Example
<use_mcp_tool>
<server_name>gpt-image-1</server_name>
<tool_name>create_image</tool_name>
<arguments>
{
"prompt": "A futuristic city skyline at sunset, digital art",
"size": "1024x1024",
"quality": "high",
"n": 1,
"background": "auto"
}
</arguments>
</use_mcp_tool>
Response
The tool returns:
- A formatted text message with details about the generated image(s)
- The image(s) as base64-encoded data
- Metadata including token usage and file paths
Tool: create_image_edit
Edits an existing image based on a text prompt and optional mask.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
image | string, object, or array | Yes | The image(s) to edit (base64 string or file path object) |
prompt | string | Yes | The text description of the desired edit (max 32,000 chars) |
mask | string or object | No | The mask that defines areas to edit (base64 string or file path object) |
size | string | No | Image size: "1024x1024" (default), "1536x1024", or "1024x1536" |
quality | string | No | Image quality: "high" (default), "medium", or "low" |
n | integer | No | Number of images to generate (1-10, default: 1) |
background | string | No | Background style: "transparent", "opaque", or "auto" (default) |
user | string | No | User identifier for OpenAI usage tracking |
Example with Base64 Encoded Image
<use_mcp_tool>
<server_name>gpt-image-1</server_name>
<tool_name>create_image_edit</tool_name>
<arguments>
{
"image": "BASE64_ENCODED_IMAGE_STRING",
"prompt": "Add a small robot in the corner",
"mask": "BASE64_ENCODED_MASK_STRING",
"quality": "high"
}
</arguments>
</use_mcp_tool>
Example with File Path
<use_mcp_tool>
<server_name>gpt-image-1</server_name>
<tool_name>create_image_edit</tool_name>
<arguments>
{
"image": {
"filePath": "C:/path/to/your/image.png"
},
"prompt": "Add a small robot in the corner",
"mask": {
"filePath": "C:/path/to/your/mask.png"
},
"quality": "high"
}
</arguments>
</use_mcp_tool>
Response
The tool returns:
- A formatted text message with details about the edited image(s)
- The edited image(s) as base64-encoded data
- Metadata including token usage and file paths
🔧 Troubleshooting
🚨 Common Issues
| Issue | Solution |
|---|---|
🖼️ MIME Type ErrorsErrors related to image format or MIME type handling |
Ensure image files have the correct extension (.png, .jpg, etc.) that matches their actual format. The server uses file extensions to determine MIME types. |
🔑 API Key IssuesAuthentication errors with OpenAI API |
Verify your OpenAI API key is correct and has access to the gpt-image-1 model. Check for any spaces or special characters that might have been accidentally included. |
🛠️ Build ErrorsIssues when building from source |
Ensure you have the correct TypeScript version installed (v5.3.3 or compatible) and that your |
📁 Output Directory IssuesProblems with saving generated images |
Check if the process has write permissions to the configured output directory. Try using an absolute path for |
🔍 Error Handling and Reporting
The MCP server includes comprehensive error handling that provides detailed information when something goes wrong. When an error occurs:
-
Error Format: All errors are returned with:
- A clear error message describing what went wrong
- The specific error code or type
- Additional context about the error when available
-
AI Assistant Behavior: When using this MCP server with AI assistants:
- The AI will always report the full error message to help with troubleshooting
- The AI will explain the likely cause of the error in plain language
- The AI will suggest specific steps to resolve the issue
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
License Summary
The MIT License is a permissive license that is short and to the point. It lets people do anything with your code with proper attribution and without warranty.
You are free to:
- Use the software commercially
- Modify the software
- Distribute the software
- Use and modify the software privately
Under the following terms:
- Include the original copyright notice and the license notice in all copies or substantial uses of the work
Limitations:
- The authors provide no warranty with the software and are not liable for any damages
🙏 Acknowledgments
Developed with ❤️ by CLOUDWERX
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.