Generate and edit images instantly using GPT Image Generator, an advanced AI image generator for creative visual content
Enables direct image generation and editing through OpenAI's gpt-image-1 model with support for text prompts, file paths, and base64 encoded inputs for creative workflows and visual content creation.
Connects to OpenAI's gpt-image-1 model to generate and edit images from text prompts, file paths, or base64 inputs. Provides direct image creation capabilities through the Model Context Protocol.
about
GPT Image Generator is a community-built MCP server published by cloudwerx-dev that provides AI assistants with tools and capabilities via the Model Context Protocol. Generate and edit images instantly using GPT Image Generator, an advanced AI image generator for creative visual content It is categorized under ai ml.
how to install
You can install GPT Image Generator in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
GPT Image Generator is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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.
```bash
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
```bash
# Set your OpenAI API key
export OPENAI_API_KEY=sk-your-openai-api-key
# Optional: Set custom output directory
export GPT_IMAGE_OUTPUT_DIR=/home/username/Pictures/ai-generated-images
# Run the server with NPX
npx -y @cloudwerxlab/gpt-image-1-mcp
```
Windows (PowerShell)
```powershell
# Set your OpenAI API key
$env:OPENAI_API_KEY = "sk-your-openai-api-key"
# Optional: Set custom output directory
$env:GPT_IMAGE_OUTPUT_DIR = "C:\Users\username\Pictures\ai-generated-images"
# Run the server with NPX
npx -y @cloudwerxlab/gpt-image-1-mcp
```
Windows (Command Prompt)
```cmd
:: Set your OpenAI API key
set OPENAI_API_KEY=sk-your-openai-api-key
:: Optional: Set custom output directory
set GPT_IMAGE_OUTPUT_DIR=C:\Users\username\Pictures\ai-generated-images
:: Run the server with NPX
npx -y @cloudwerxlab/gpt-image-1-mcp
```
## 🔌 Integration with MCP Clients
### 🛠️ Setting Up in an MCP Client
Step 1: Locate Settings File
For Roo: c:\Users\<username>\AppData\Roaming\Code\User\globalStorageooveterinaryinc.roo-cline\settings\mcp_settings.json
For VS Code MCP Extension: Check your extension documentation for the settings file location
For Cursor: ~/.config/cursor/mcp_settings.json (Linux/macOS) or %APPDATA%\Cursor\mcp_settings.json (Windows)
For Augment: ~/.config/augment/mcp_settings.json (Linux/macOS) or %APPDATA%\Augment\mcp_settings.json (Windows)
For Windsurf: ~/.config/windsurf/mcp_settings.json (Linux/macOS) or %APPDATA%\Windsurf\mcp_settings.json (Windows)
Step 2: Add Configuration
Add the following configuration to the mcpServers object:
```json
{
"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
> **Note**: For Windows paths, use double backslashes (`\`) to escape the backslash character in JSON. For Linux/macOS, use forward slashes (`/`).
## ✨ Features
🎨 Core Tools
create_image: Generate new images from text prompts
create_image_edit: Edit existing images with text prompts and masks
🚀 Key Benefits
Simple integration with MCP clients
Full access to OpenAI's gpt-image-1 capabilities
Streamlined workflow for AI image generation
### 💡 Enhanced Capabilities
📊 Output & Formatting
✅ Beautifully Formatted Output: Responses include emojis and detailed information
✅ Automatic Image Saving: All generated images saved to disk for easy access
✅ Detailed Token Usage: View token consumption for each request
⚙️ Configuration & Handling
✅ Configurable Output Directory: Customize where images are saved
✅ File Path Support: Edit images using file paths instead of base64 encoding
✅ Comprehensive Error Handling: Detailed error reporting with specific error codes, descriptions, and troubleshooting suggestions
## 🔄 How It Works
🖼️ Image Generation
✏️ Image Editing
Server receives prompt and parameters
Calls OpenAI API using gpt-image-1 model
API returns base64-encoded images
Server saves images to configured directory
Returns formatted response with paths and metadata
Server receives image, prompt, and optional mask
For file paths, reads and prepares files for API
Uses direct curl command for proper MIME handling
API returns base64-encoded edited images
Server saves images to configured directory
Returns formatted response with paths and metadata
### 📁 Output Directory Behavior
📂 Storage Location
🔹 Default Location: User's Pictures folder under gpt-image-1 subfolder (e.g., C:\Users\username\Pictures\gpt-image-1 on Windows)
🔹 Custom Location: Set via GPT_IMAGE_O
---
FAQ
What is the GPT Image Generator MCP server?
GPT Image Generator is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for GPT Image Generator?
This profile displays 49 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
✓
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
⚠Feature not working: Read server docs, check required parameters
⚠Performance issues: Monitor resource usage, check for network latency
⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
+Read server documentation thoroughly before setup
+Start with simple use cases to validate functionality
+Test in non-production environment first
+Monitor resource usage and performance
+Keep servers updated for bug fixes and new features
+Document configuration for team members
+Use environment variables for sensitive configuration
✗ Don't
−Don't grant overly permissive access to MCP servers
−Don't skip reading security considerations in docs
−Don't expose sensitive data without proper controls
−Don't run untrusted MCP servers without code review
−Don't ignore error messages—investigate root cause
💡 Pro Tips
★Combine multiple MCP servers for powerful workflows
★Create custom MCP servers for your specific needs
★Share successful configurations with team
★Use MCP inspector for debugging
★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Model Context Protocol (MCP)
JSON-RPC 2.0
stdio or HTTP transport
Compatibility
Claude Desktop
Cursor IDE
Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
→Tool composition: Chain multiple MCP tools in workflows
→Context augmentation: Provide AI with relevant external data
→Action delegation: Let AI execute tasks on external systems
→Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)
No comments yet — start the thread.
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