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.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。