ComfyUI MCP Server

@zuojianghua

关于 ComfyUI MCP Server

generate_image and other workflows

基本信息

分类

其他

运行时

python

传输方式

stdio

发布者

zuojianghua

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "ComfyUI_MCP_Server": {
      "disabled": false,
      "timeout": 600,
      "command": "python",
      "args": [
        "D:\\code\\comfyui_mcp_server\\server.py"
      ],
      "env": {
        "COMFY_URL": "http://127.0.0.1:8188/"
      },
      "transportType": "stdio"
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is ComfyUI MCP Server?

A Model Context Protocol (MCP) server for ComfyUI that provides image generation and prompt optimization services.

How to use ComfyUI MCP Server?

Configure the server in your MCP client using the provided JSON snippet, setting the COMFY_URL environment variable to your running ComfyUI instance (default http://127.0.0.1:8188/). The server runs as a Python script via stdio transport. Ensure Python 3.7+ and the FastMCP library are installed, and that a ComfyUI instance is active.

Key features of ComfyUI MCP Server

Image generation from text prompts using ComfyUI workflows
Prompt optimization to improve generation results
Automatic image dimension adjustment (multiples of 8)
Random seed generation for diverse outputs
Returns both local file paths and online accessible URLs

Use cases of ComfyUI MCP Server

Generate images from textual descriptions via ComfyUI’s text_to_image workflows
Enhance prompt quality to achieve better image generation results

FAQ from ComfyUI MCP Server

What are the requirements to run the server?

Python 3.7+, a running ComfyUI instance, and the FastMCP library. The ComfyUI API must be accessible at the URL specified in COMFY_URL.

How does the server handle image dimensions?

It automatically adjusts image dimensions to multiples of 8 to ensure compatibility with ComfyUI’s model requirements.

What transport does the server use?

The server uses the stdio transport, as shown in the default MCP configuration example.

Are results returned as files or URLs?

Both local file paths and publicly accessible URLs are provided for each generated image.

How are diverse outputs generated?

A random seed is generated per request, producing varied results from the same prompt.

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