What can you do with this server?

This server provides an MCP interface to OpenAI's GPT-Image-1 model, enabling: * Image Generation: Create images from text prompts with customizable size, quality, background, number of images, and output format * Image Editing: Modify existing images using text prompts with optional masks, supporting both base64 and file paths * Output Management: Automatically save generated/edited images to a configurable directory * Integration: Works seamlessly with MCP-compatible clients for streamlined AI image workflows * Reporting: Provides token usage information and comprehensive error handling with troubleshooting advice

Which integrations are available for this server?

Uses curl commands for proper MIME handling when working with image files, especially for the image editing functionality. Runs as a Node.js application, with the MCP server requiring Node.js v14+ to function properly. Available as an npm package that can be installed globally or run directly with npx, making it easy to integrate with various MCP clients. Provides access to OpenAI's gpt-image-1 model for generating and editing images through text prompts, with capabilities for controlling image size, quality, background style, and output formats.

How do I use GPT Image 1 MCP?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GPT Image 1 MCP create a logo for a coffee shop called 'Morning Brew'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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GPT Image 1 MCP

by CLOUDWERX-DEV

Overview Schema Source Related Servers Discussions Admin

JavaScript

Hybrid

🚀 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

Related MCP server: OpenAI 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

Available Tools

2 tools

create_imageD

ParametersJSON Schema

Name	Required	Description	Default
`prompt`	Yes
`background`	No
`n`	No
`output_compression`	No
`output_format`	No
`quality`	No
`size`	No
`user`	No
`moderation`	No

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_image_editD

ParametersJSON Schema

Name	Required	Description	Default
`image`	Yes
`prompt`	Yes
`background`	No
`mask`	No
`n`	No
`quality`	No
`size`	No
`user`	No

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

2 tool updatesMay 9, 2025
- First observedcreate_image
- First observedcreate_image_edit

Deploy Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

D1.5/5.0

Scored 2026-04-18 10:10 across 2 tools

Disambiguation2/5

The two tools have overlapping purposes—both involve creating images—and without descriptions, it's unclear how they differ. 'create_image_edit' suggests editing an existing image, but this could easily be confused with the base 'create_image' tool, leading to potential misselection.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with 'create_image' as the base, and 'create_image_edit' extends this logically. The naming is predictable and clear, with no deviations in style or convention.

Tool Count2/5

With only 2 tools, the server feels thin for an image-related domain, which typically requires operations like listing, retrieving, updating, or deleting images. This limited set may not support common workflows, making it under-scoped.

Completeness1/5

The tool surface is severely incomplete for an image server; there are no tools for reading, updating, deleting, or managing images beyond creation and editing. This will cause significant agent failures in handling image lifecycles or varied tasks.

Maintenance

ActivityInactive

ResponsivenessUnresponsive

Resources

Need Help?

Reddit Discussion

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🚀 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

Available Tools

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Image Toolkit MCP Server

OpenAI MCP

Gemini MCP Image Generation

DALL-E 3 MCP Server

Appeared in Searches

Tool: `create_image`

Tool: `create_image_edit`