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GPT Image 1 MCP

Name: GPT Image 1 MCP
Author: CLOUDWERX-DEV

by CLOUDWERX-DEV

Overview Schema Source Related Servers Discussions Admin

JavaScript

Hybrid

🚀 快速入门

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

📋 先决条件

🔑 环境变量

💻 NPX 用法示例

# 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

🔌 与 MCP 客户端集成

🛠️ 在 MCP 客户端中设置

{
  "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"
      }
    }
  }
}

不同操作系统的示例配置

{
  "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"
      }
    }
  }
}

注意：对于 Windows 路径，请使用双反斜杠 ( \\ ) 来转义 JSON 中的反斜杠字符。对于 Linux/macOS，请使用正斜杠 ( / )。

✨ 特点

💡 增强功能

🔄 工作原理

📁 输出目录行为

安装和使用

NPM 包

此软件包可在 npm 上获取： @cloudwerxlab/gpt-image-1-mcp

您可以全局安装它：

npm install -g @cloudwerxlab/gpt-image-1-mcp

或者直接使用 npx 运行它，如快速入门部分所示。

工具： `create_image`

根据文本提示生成新图像。

参数

范围	类型	必需的	描述
`prompt`	细绳	是的	要生成的图像的文本描述（最多 32,000 个字符）
`size`	细绳	不	图像尺寸：“1024x1024”（默认）、“1536x1024”或“1024x1536”
`quality`	细绳	不	图像质量：“高”（默认）、“中”或“低”
`n`	整数	不	生成的图像数量（1-10，默认值：1）
`background`	细绳	不	背景样式：“透明”、“不透明”或“自动”（默认）
`output_format`	细绳	不	输出格式：“png”（默认）、“jpeg”或“webp”
`output_compression`	整数	不	压缩级别（0-100，默认值：0）
`user`	细绳	不	OpenAI 使用情况跟踪的用户标识符
`moderation`	细绳	不	审核级别：“低”或“自动”（默认）

例子

<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>

该工具返回：

包含所生成图像详细信息的格式化文本消息
以 base64 编码的数据格式显示图像
元数据包括令牌使用情况和文件路径

工具： `create_image_edit`

根据文本提示和可选掩码编辑现有图像。

参数

范围	类型	必需的	描述
`image`	字符串、对象或数组	是的	要编辑的图像（base64字符串或文件路径对象）
`prompt`	细绳	是的	所需编辑的文本描述（最多 32,000 个字符）
`mask`	字符串或对象	不	定义要编辑的区域的掩码（base64字符串或文件路径对象）
`size`	细绳	不	图像尺寸：“1024x1024”（默认）、“1536x1024”或“1024x1536”
`quality`	细绳	不	图像质量：“高”（默认）、“中”或“低”
`n`	整数	不	生成的图像数量（1-10，默认值：1）
`background`	细绳	不	背景样式：“透明”、“不透明”或“自动”（默认）
`user`	细绳	不	OpenAI 使用情况跟踪的用户标识符

Base64 编码图像示例

<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>

文件路径示例

<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>

该工具返回：

包含已编辑图像详细信息的格式化文本消息
编辑后的图像作为 base64 编码数据
元数据包括令牌使用情况和文件路径

🔧 故障排除

🚨常见问题

🔍 错误处理和报告

MCP 服务器包含全面的错误处理功能，可在出现问题时提供详细信息。发生错误时：

错误格式：所有错误均返回：
- 清晰的错误信息，描述出了什么问题
- 具体的错误代码或类型
- 如果有关于错误的附加上下文
AI 助手行为：当此 MCP 服务器与 AI 助手一起使用时：
- 人工智能将始终报告完整的错误消息以帮助排除故障
- 人工智能将用通俗易懂的语言解释错误的可能原因
- 人工智能将建议解决问题的具体步骤

📄 许可证

🙏 致谢

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

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.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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🚀 快速入门

📋 先决条件

🔑 环境变量

💻 NPX 用法示例

🔌 与 MCP 客户端集成

🛠️ 在 MCP 客户端中设置

不同操作系统的示例配置

✨ 特点

💡 增强功能

🔄 工作原理

📁 输出目录行为

安装和使用

NPM 包

工具： create_image

参数

例子

回复

工具： create_image_edit

参数

Base64 编码图像示例

文件路径示例

回复

🔧 故障排除

🚨常见问题

🔍 错误处理和报告

📄 许可证

🙏 致谢

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

工具： `create_image`

工具： `create_image_edit`