ComfyUI MCP Server

1. Overview

- A server implementation for integrating ComfyUI with MCP.
- ⚠️ IMPORTANT: This server requires a running ComfyUI server.
- You must either host your own ComfyUI server,
- or have access to an existing ComfyUI server address.

<a href="https://glama.ai/mcp/servers/@Overseer66/comfyui-mcp-server">

</a>

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2. Debugging

### 2.1 ComfyUI Debugging

  python src/test_comfyui.py
  

### 2.2 MCP Debugging

  mcp dev src/server.py
  

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3. Installation and Configuration

### 3.1 ComfyUI Configuration

- Edit src/.env to set ComfyUI host and port:

      COMFYUI_HOST=localhost
      COMFYUI_PORT=8188
      

### 3.2 Adding Custom Workflows

- To add new tools, place your workflow JSON files in the workflows directory and declare them as new tools in the system.

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4. Built-in Tools

- text_to_image

- Returns only the URL of the generated image.
- To get the actual image:
- Use the download_image tool, or
- Access the URL directly in your browser.

- download_image

- Downloads images generated by other tools (like text_to_image) using the image URL.

- run_workflow_with_file

- Run a workflow by providing the path to a workflow JSON file.

        # You should ask to agent like this.
        Run comfyui workflow with text_to_image.json
        

- example image of CursorAI

- run_workflow_with_json

- Run a workflow by providing the workflow JSON data directly.

        # You should ask to agent like this.
        Run comfyui workflow with this 
        {
          "3": {
              "inputs": {
                  "seed": 156680208700286,
                  "steps": 20,
            ... (workflow JSON example)
        }
        

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5. How to Run

### 5.1 Using UV (Recommended)

- Example mcp.json:

      {
        "mcpServers": {
          "comfyui": {
            "command": "uv",
            "args": [
              "--directory",
              "PATH/MCP/comfyui",
              "run",
              "--with",
              "mcp",
              "--with",
              "websocket-client",
              "--with",
              "python-dotenv",
              "mcp",
              "run",
              "src/server.py:mcp"
            ]
          }
        }
      }
      

### 5.2 Using Docker

- Downloading images to a local folder with download_image may be difficult since the Docker container does not share the host filesystem.
- When using Docker, consider:
1. Set RETURN_URL=false in .env to receive image data as bytes.
2. Set COMFYUI_HOST in .env to the appropriate address (e.g., host.docker.internal or your server's IP).
3. Note: Large image payloads may exceed response limits when using binary data.

#### 5.2.1 Build Docker Image

  # First build image
  docker image build -t mcp/comfyui .
  

  {
    "mcpServers": {
      "comfyui": {
        "command": "docker",
        "args": [
          "run",
          "-i",
          "--rm",
          "-p",
          "3001:3000",
          "mcp/comfyui"
        ]
      }
    }
  }
  

#### 5.2.2 Using Existing Images

Also you can use prebuilt image.

  {
    "mcpServers": {
      "comfyui": {
        "command": "docker",
        "args": [
          "run",
          "-i",
          "--rm",
          "-p",
          "3001:3000",
          "overseer66/mcp-comfyui"
        ]
      }
    }
  }
  

#### 5.2.3 Using SSE Transport

1. Run the SSE server with Docker:

      docker run -i --rm -p 8001:8000 overseer66/mcp-comfyui-sse
      

2. Configure mcp.json (change localhost to your IP or domain if needed):

      {
        "mcpServers": {
          "comfyui": {
            "url": "http://localhost:8001/sse" 
          }
        }
      }
      

> NOTE: When adding new workflows as tools, you need to rebuild and redeploy the Docker images to make them available.

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