Gpt Image 1

  <p>Node.js (v14 or higher)</p>
</td>
<td width="50%" align="center">
  
  <p>OpenAI API key with access to gpt-image-1</p>
</td>

🔑 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

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

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

      <p>For providing the gpt-image-1 model</p>
    </a>
  </td>
  <td align="center">
    <a href="https://github.com/model-context-protocol/mcp">
      
      <p>For the protocol specification</p>
    </a>
  </td>
</tr>