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"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@Overseer66/comfyui-mcp-server/badge" alt="ComfyUI Server MCP server" /> </a>

---

2. Debugging

2.1 ComfyUI Debugging

python src/test_comfyui.py

2.2 MCP Debugging

mcp dev src/server.py

---

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.

---

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)
    }
    

---

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.

---