README.md•4.12 kB
# 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.
---
## 2. Debugging
### 2.1 ComfyUI Debugging
```bash
python src/test_comfyui.py
```
### 2.2 MCP Debugging
```bash
mcp dev src/server.py
```
---
## 3. Installation and Configuration
### 3.1 ComfyUI Configuration
- Edit `src/.env` to set ComfyUI host and port:
```env
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`:
```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
```bash
# First build image
docker image build -t mcp/comfyui .
```
```json
{
"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.
```json
{
"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:
```bash
docker run -i --rm -p 8001:8000 overseer66/mcp-comfyui-sse
```
2. Configure `mcp.json` (change localhost to your IP or domain if needed):
```json
{
"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.
---