comfyui-mcp-server-node
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@comfyui-mcp-server-nodegenerate an image of a cat sitting on a windowsill"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
comfyui-mcp-server-node
This is improved node.js version of Joe Norton's python project (https://github.com/joenorton/comfyui-mcp-server).
A lightweight MCP (Model Context Protocol) server that bridges AI agents (like Cursor, Claude, etc.) with a local ComfyUI instance. It enables AI agents to generate and iteratively refine images, audio, and video through conversational tool calls.
Features
Image Generation: Generate images using Stable Diffusion through ComfyUI
Audio Generation: Generate audio/music using AceStep workflow
Workflow System: PARAM_* placeholder system for easy workflow customization
Asset Management: Track, view, and manage generated assets
Job Management: Monitor queue, check job status, cancel jobs
Configuration: List available models
Quick Start
Option 1: Via npx (for MCP clients)
No local clone needed. Add to your MCP client configuration (Cursor, Claude, etc.):
"comfyui": {
"command": "npx",
"args": ["-y", "comfyui-mcp-server-node"],
"env": {
"COMFYUI_URL": "http://localhost:8188",
"COMFY_MCP_WORKFLOW_DIR": "/path/to/workflows",
"COMFY_MCP_ASSET_TTL_HOURS": "24"
}
}Note: ComfyUI must be running at
COMFYUI_URLbefore the MCP client connects.
Option 2: Local development
git clone https://github.com/yar3333/comfyui-mcp-server-node.git
cd comfyui-mcp-server-node
npm install
npm run buildThen start:
Command | Mode |
| stdio (for MCP clients) |
| stdio with ts-node |
Configuration
Environment Variables
Variable | Description | Default |
| ComfyUI base URL |
|
| Path to workflow directory |
|
| Asset time-to-live in hours |
|
API Tools
Generation Tools
Tool | Description |
| Available workflows automatically published as tools |
| Regenerate a previously generated asset |
Viewing Tools
Tool | Description |
| View a generated image inline in chat |
Job Management Tools
Tool | Description |
| Get current queue status from ComfyUI |
| Get job status by prompt_id |
| Wait for a job to complete with timeout |
| List generated assets with optional filtering |
| Get full metadata for a specific asset |
| Cancel a running job by prompt_id |
Configuration Tools
Tool | Description |
| List available checkpoint models from ComfyUI |
| List available UNet models in standard (safetensors) format |
| List available UNet models in GGUF format |
Workflow Tools
Tool | Description |
| List available workflows in the workflow directory |
| Run a specific workflow with parameter overrides |
Workflow System
Workflows are stored as JSON files in the workflows/ directory. The system automatically discovers workflows and exposes them as MCP tools. Parameters are defined using the PARAM_* placeholder system:
PARAM_INT_SEED- Integer parameter for seedPARAM_FLOAT_CFG- Float parameter for CFG scalePARAM_STR_SAMPLER_NAME- String parameter for sampler namePARAM_PROMPT- String parameter for prompt
Test
Prerequisites: ComfyUI running at http://localhost:8188, server built and started.
# Run the test client
npx ts-node test_client.ts
# With custom prompt
npx ts-node test_client.ts -p "a beautiful sunset over mountains"# Run unit tests
npm testProject Structure
comfyui-mcp-server-node/
├── src/
│ ├── comfyui_client.ts # HTTP client for ComfyUI API
│ ├── asset_processor.ts # Image processing utilities
│ ├── server.ts # Main entry point
│ ├── models/ # Data models
│ │ ├── asset.ts
│ │ └── workflow.ts
│ ├── managers/ # Manager classes
│ │ ├── workflow_manager.ts
│ │ └── asset_registry.ts
│ └── tools/ # MCP tool implementations
│ ├── helpers.ts
│ ├── generation.ts
│ ├── asset.ts
│ ├── job.ts
│ ├── configuration.ts
│ └── workflow.ts
├── workflows/ # Workflow JSON files
├── test_client.ts # Test client
├── package.json
├── tsconfig.json
└── README.mdChangelog
v2.x.x
add
list_checkpoint_modelstooladd
list_unet_modelstooladd
list_unet_gguf_modelstoolremove http mode (stdio looks enough)
remove
list_modelstoolremove publish system (caused confusion for AI agents; use custom output node instead)
remove default parameters (caused confusion for AI agents; use regular PARAM_* instead)
remove output folder setting (use custom output node instead; for example: https://gist.github.com/kevinjwesley-Collab/a548ee5e6244ebf905f0669e1d7d4958)
v1.x.x
add
wait_for_jobtool
License
MIT
Author
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