comfyui-mcp
Exposes a remote ComfyUI instance for image generation through an on-demand GPU wrapper, allowing users to generate PNG images from prompts and manage the ComfyUI lifecycle via MCP tools.
README
ComfyUI OpenAI/FAL-Compatible Images API
A single-container, multi-protocol image-generation backend backed by ComfyUI. The container starts ComfyUI on the first generation request, keeps it alive while you use it, and cold-stops it after an idle timeout so GPU memory is freed when nothing is generating.
Supported protocols:
- OpenAI-compatible —
POST /v1/images/generations,GET /v1/models - FAL.ai-compatible —
POST /fal/run/{model_id},POST /fal/queue/{model_id},GET /fal/queue/{model_id}/requests/{request_id}/status - Wrapper native —
POST /generate,GET /health,GET /status,POST /stop
Generated images are persisted under ComfyUI/output and served at /outputs/{filename}, so response_format: url and FAL-style responses return real URLs.
Endpoints
OpenAI-compatible
POST /v1/images/generations accepts the standard OpenAI request shape:
{
"prompt": "a photo of an astronaut riding a horse on the moon",
"n": 1,
"size": "512x512",
"response_format": "b64_json"
}
Supported parameters:
prompt(required)n(1–4, default 1)size—"WIDTHxHEIGHT", e.g."512x512"(default),"1024x1024"response_format—"b64_json"(default) or"url"model— accepted but currently informationalquality,style,user— accepted but ignored- ComfyUI overrides:
negative_prompt,width,height,steps,cfg,seed
FAL.ai-compatible
POST /fal/run/{model_id} accepts a FAL-style payload:
{
"prompt": "a serene mountain landscape with cherry blossoms",
"image_size": "landscape_16_9",
"num_images": 1,
"seed": 42
}
Supported fields:
prompt(required)negative_promptimage_size— e.g.square_hd,landscape_16_9,portrait_16_9aspect_ratio— e.g.1:1,16:9,9:16width/height(override size/aspect)num_inference_steps→ mapped to ComfyUI stepsguidance_scale→ mapped to ComfyUI cfgseednum_images(1–4)image_url/reference_image_urls— rejected with a clear error (editing is not supported)
Response shape mirrors FAL:
{
"images": [
{
"url": "http://192.168.2.51:8002/outputs/abc123.png",
"width": 1536,
"height": 1024,
"content_type": "image/png"
}
],
"prompt": "a serene mountain landscape with cherry blossoms",
"seed": 42,
"has_nsfw_concepts": [false]
}
Running the container
cd wrapper
# place v1-5-pruned-emaonly.safetensors in this directory first
docker compose up -d --build
The API is exposed on port 8002 (ComfyUI direct is on 8190).
Environment variables
| Variable | Default | Description |
|---|---|---|
IDLE_TIMEOUT |
300 |
Seconds of inactivity before ComfyUI is stopped |
COMFYUI_PORT |
8188 |
Internal ComfyUI port |
API_PORT |
8000 |
Internal API port |
PUBLIC_URL |
(request base URL) | Base URL used for generated image links, e.g. http://192.168.2.51:8002 |
OPENAI_API_KEY |
(none) | Optional Bearer token required on OpenAI endpoints |
MODEL_ID |
comfyui-sd1-5 |
Model id advertised by /v1/models |
Using with Hermes
Hermes supports FAL.ai as a backend. Point Hermes at this container by making fal.run / queue.fal.run resolve to http://192.168.2.51:8002. The easiest way is to add a local DNS or proxy rule, or set the FAL client base URL if Hermes/FAL's SDK exposes one.
Set FAL_KEY to any non-empty dummy value in Hermes config, pick any FAL model id (e.g. fal-ai/flux-2/klein/9b), and generation requests will be handled by your local ComfyUI backend.
Example requests
OpenAI:
curl -X POST http://127.0.0.1:8002/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "a photo of an astronaut riding a horse on the moon",
"size": "512x512",
"response_format": "b64_json"
}'
FAL:
curl -X POST http://127.0.0.1:8002/fal/run/fal-ai/flux-2/klein/9b \
-H "Content-Type: application/json" \
-d '{
"prompt": "a serene mountain landscape with cherry blossoms",
"image_size": "landscape_16_9"
}'
Cold-start behavior
The first request after the wrapper has been idle will trigger a ComfyUI startup. The API server waits for ComfyUI to start and generate the image, so the client may observe a longer response time on the first call. Subsequent calls are fast until the idle timeout elapses and ComfyUI is stopped again.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.