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GPT Image 1 MCP

by CLOUDWERX-DEV
npmGitHub
JavaScript
MIT License
545
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🚀 Quick Start

npx -y @cloudwerxlab/gpt-image-1-mcp

📋 Prerequisites

🔑 Environment Variables

💻 Example Usage with NPX

# 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
# 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
:: 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

{ "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
{ "mcpServers": { "gpt-image-1": { "command": "npx", "args": ["-y", "@cloudwerxlab/gpt-image-1-mcp"], "env": { "OPENAI_API_KEY": "sk-your-openai-api-key", "GPT_IMAGE_OUTPUT_DIR": "C:\\Users\\username\\Pictures\\ai-generated-images" } } } }
{ "mcpServers": { "gpt-image-1": { "command": "npx", "args": ["-y", "@cloudwerxlab/gpt-image-1-mcp"], "env": { "OPENAI_API_KEY": "sk-your-openai-api-key", "GPT_IMAGE_OUTPUT_DIR": "/home/username/Pictures/ai-generated-images" } } } }

Note: For Windows paths, use double backslashes (\\) to escape the backslash character in JSON. For Linux/macOS, use forward slashes (/).

✨ Features

💡 Enhanced Capabilities

🔄 How It Works

📁 Output Directory Behavior

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
ParameterTypeRequiredDescription
promptstringYesThe text description of the image to generate (max 32,000 chars)
sizestringNoImage size: "1024x1024" (default), "1536x1024", or "1024x1536"
qualitystringNoImage quality: "high" (default), "medium", or "low"
nintegerNoNumber of images to generate (1-10, default: 1)
backgroundstringNoBackground style: "transparent", "opaque", or "auto" (default)
output_formatstringNoOutput format: "png" (default), "jpeg", or "webp"
output_compressionintegerNoCompression level (0-100, default: 0)
userstringNoUser identifier for OpenAI usage tracking
moderationstringNoModeration 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
ParameterTypeRequiredDescription
imagestring, object, or arrayYesThe image(s) to edit (base64 string or file path object)
promptstringYesThe text description of the desired edit (max 32,000 chars)
maskstring or objectNoThe mask that defines areas to edit (base64 string or file path object)
sizestringNoImage size: "1024x1024" (default), "1536x1024", or "1024x1536"
qualitystringNoImage quality: "high" (default), "medium", or "low"
nintegerNoNumber of images to generate (1-10, default: 1)
backgroundstringNoBackground style: "transparent", "opaque", or "auto" (default)
userstringNoUser 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

🔍 Error Handling and Reporting

The MCP server includes comprehensive error handling that provides detailed information when something goes wrong. When an error occurs:

  1. 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
  2. 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

🙏 Acknowledgments

Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

Un servidor de protocolo de contexto de modelo que permite generar y editar imágenes utilizando el modelo gpt-image-1 de OpenAI, lo que permite a los asistentes de IA crear y modificar imágenes a partir de indicaciones de texto.

  1. 📋 Requisitos previos
    1. 🔑 Variables de entorno
      1. 💻 Ejemplo de uso con NPX
        1. 🔌 Integración con clientes MCP
          1. 🛠️ Configuración en un cliente MCP
        2. ✨ Características
          1. 💡 Capacidades mejoradas
        3. 🔄 Cómo funciona
          1. 📁 Comportamiento del directorio de salida
        4. Instalación y uso
          1. Paquete NPM
          2. Herramienta: create_image
          3. Herramienta: create_image_edit
        5. 🔧 Solución de problemas
          1. 🚨 Problemas comunes
          2. 🔍 Manejo y generación de informes de errores
        6. 📄 Licencia
          1. 🙏 Agradecimientos

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