GPT Изображение 1 MCP

🚀 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

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

🔍 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

🙏 Acknowledgments

Install Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

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

Сервер Model Context Protocol, позволяющий генерировать и редактировать изображения с использованием модели OpenAI gpt-image-1, что позволяет помощникам ИИ создавать и изменять изображения с помощью текстовых подсказок.

Related Resources

Reddit Discussion about this server

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GPT Image 1 MCP

🚀 Quick Start

📋 Prerequisites

🔑 Environment Variables

💻 Example Usage with NPX

🔌 Integration with MCP Clients

🛠️ Setting Up in an MCP Client

Example Configurations for Different Operating Systems

✨ Features

💡 Enhanced Capabilities

🔄 How It Works

📁 Output Directory Behavior

Installation & Usage

NPM Package

Tool: `create_image`

Parameters

Example

Response

Tool: `create_image_edit`

Parameters

Example with Base64 Encoded Image

Example with File Path

Response

🔧 Troubleshooting

🚨 Common Issues

🔍 Error Handling and Reporting

📄 License

🙏 Acknowledgments

Tools

Related Resources

Related MCP Servers

Ideogram MCP Server

Image Toolkit MCP Server

OpenAI MCP

OpenAI Image Generation MCP Server

MCP directory API

🚀 Quick Start

📋 Prerequisites

🔑 Environment Variables

💻 Example Usage with NPX

🔌 Integration with MCP Clients

🛠️ Setting Up in an MCP Client

Example Configurations for Different Operating Systems

✨ Features

💡 Enhanced Capabilities

🔄 How It Works

📁 Output Directory Behavior

Installation & Usage

NPM Package

Tool: create_image

Parameters

Example

Response

Tool: create_image_edit

Parameters

Example with Base64 Encoded Image

Example with File Path

Response

🔧 Troubleshooting

🚨 Common Issues

🔍 Error Handling and Reporting

📄 License

🙏 Acknowledgments

Tools

Related Resources

Related MCP Servers

Ideogram MCP Server

Image Toolkit MCP Server

OpenAI MCP

OpenAI Image Generation MCP Server

MCP directory API

Tool: `create_image`

Tool: `create_image_edit`