Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
lightweight Python-based MCP (Model Context Protocol) server for local ComfyUI
| Date | Stars |
|---|---|
| 2026-07-31 | 393 |
| 2026-08-06 | 396 |
Today
+3 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# ComfyUI MCP Server
> Generate and refine AI images/audio/video through natural conversation
A lightweight MCP (Model Context Protocol) server that lets AI agents generate and iteratively refine images, audio, and video using a local ComfyUI instance.
You run the server, connect a client, and issue tool calls. Everything else is optional depth.
---
## Quick Start (2–3 minutes)
This proves everything is working.
### 1) Clone and set up
```bash
git clone https://github.com/joenorton/comfyui-mcp-server.git
cd comfyui-mcp-server
pip install -r requirements.txt
```
### 2) Start ComfyUI
Make sure ComfyUI is installed and running locally.
```bash
cd <ComfyUI_dir>
python main.py --port 8188
```
### 3) Run the MCP server
From the repository directory:
```bash
python server.py
```
The server listens at:
```
http://127.0.0.1:9000/mcp
```
### 4) Verify it works (no AI client required)
Run the included test client:
```bash
# Use default prompt
python test_client.py
# Or provide your own prompt
python test_client.py -p "a beautiful sunset over mountains"
python test_client.py --prompt "a cat on a mat"
```
`test_client.py` will:
* connect to the MCP server
* list available tools
* fetch and display server defaults (width, height, steps, model, etc.)
* run `generate_image` with your prompt (or a default)
* automatically use server defaults for all other parameters
* print the resulting asset information
If this step succeeds, the system is working.
**Note:** The test client respects server defaults configured via config files, environment variables, or `set_defaults` calls. Only the `prompt` parameter is required; all other parameters use server defaults automatically.
That’s it.
---
## Use with an AI Agent (Cursor / Claude / n8n)
Once the server is running, you can connect it to an AI client.
Create a project-scoped `.mcp.json` file:
```json
{
"mcpServers": {
"comfyui-mcp-server": {
"type": "streamable-http",
"url": "http://127.0.0.1:9000/mcp"
}
}
}
```
**Note:** Some clients use `"type": "http"` instead of `"streamable-http"`. Both work with this server. If auto-discovery doesn't work, try changing the type field.
Restart your AI client. You can now call tools such as:
* `generate_image`
* `view_image`
* `regenerate`
* `get_job`
* `list_assets`
This is the primary intended usage mode.
---
## What You Can Do After It Works
Once you’ve confirmed the server runs and a client can connect, the system supports:
* Iterative refinement via `regenerate` (no re-prompting)
* Explicit asset identity for reliable follow-ups
* Job polling and cancellation for long-running generations
* Optional image injection into the AI’s context (`view_image`)
* Auto-discovered ComfyUI workflows with parameter exposure
* Configurable defaults to avoid repeating common settings
Everything below builds on the same basic loop you just tested.
## Migration Notes (Previous Versions)
If you’ve used earlier versions of this project, a few things have changed.
### What’s the Same
- You still run a local MCP server that delegates execution to ComfyUI
- Workflows are still JSON files placed in the `workflows/` directory
- Image generation behavior is unchanged at its core
### What’s New
- **Streamable HTTP transport** replaces the older WebSocket-based approach
- **Explicit job management** (`get_job`, `get_queue_status`, `cancel_job`)
- **Asset identity** instead of ad-hoc URLs (stable across hostname changes)
- **Iteration support** via `regenerate` (replay with parameter overrides)
- **Optional visual feedback** for agents via `view_image`
- **Configurable defaults** to avoid repeating common parameters
### What Changed Conceptually
Earlier versions were a thin request/response bridge.
The current version is built around **iteration** and **stateful control loops**.
You can still generate an image with a single call, but you now have the option to:
- refer back to specific outputs
- refine results without Excerpt of 12,410 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:eda8f4d36623c02c, topic:mcp-server, desc:model context protocol, name:mcp server
matched fp:eda8f4d36623c02c, topic:comfyui