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comfyui-mcp-server

by joenorton·313·Score 50

Lightweight Python MCP server for local ComfyUI that enables AI agents to generate and iteratively refine images, audio, and video.

ai-llmmediadeveloper-tools
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Overview

ComfyUI MCP Server provides a bridge between AI agents and local ComfyUI installations, enabling natural language control over media generation. The server exposes various tools including image generation, asset management, job control, and workflow execution. It supports iterative refinement through its regenerate functionality and provides asset identity for reliable follow-up operations. The architecture uses a streamable HTTP transport and supports both simple single-shot generation and complex stateful control loops for more advanced use cases.

Try asking AI

After installing, here are 8 things you can ask your AI assistant:

you:AI assistants creating website hero images and logos with precise control
you:Content generation for social media platforms with iterative refinement
you:Game asset development with consistent style variations
you:Music generation from lyrics and tags
you:Automated media generation for content creators
you:What is the difference between generate_image and regenerate?
you:Can I use custom ComfyUI workflows with this server?
you:How are assets identified and referenced?

When to choose this

Choose when working with ComfyUI and want AI agents to generate/refine media assets through natural conversation.

When NOT to choose this

Don't choose if you need cloud-based AI generation, want to avoid local setup complexity, or require multi-user collaboration features.

Tools this server exposes

12 tools extracted from the README
  • generate_image

    Generate images from text prompts

  • generate_song

    Generate audio from tags and lyrics

  • regenerate

    Regenerate an existing asset with parameter overrides

  • view_image

    View generated images inline in the conversation

  • get_job

    Poll job completion status by prompt_id

  • list_assets

    Browse recently generated assets

  • get_asset_metadata

    Get provenance and parameters for an asset

  • cancel_job

    Cancel a queued or running job

  • list_models

    List available ComfyUI models

  • set_defaults

    Set default values for generation parameters

  • list_workflows

    List all available ComfyUI workflows

  • publish_asset

    Publish a generated asset to the web directory

Comparable tools

comfyui-nodesstable-diffusion-mcprunway-mcp

Installation

Quick Installation

1) Clone and install dependencies:

git clone https://github.com/joenorton/comfyui-mcp-server.git
cd comfyui-mcp-server
pip install -r requirements.txt

2) Start ComfyUI locally:

cd <ComfyUI_dir>
python main.py --port 8188

3) Run the MCP server:

python server.py

4) Configure your AI client (e.g., Claude Desktop, Cursor) with:

{
  "mcpServers": {
    "comfyui-mcp-server": {
      "type": "streamable-http",
      "url": "http://127.0.0.1:9000/mcp"
    }
  }
}

FAQ

What is the difference between generate_image and regenerate?
generate_image creates a new image from a prompt, while regenerate recreates an existing asset with optional parameter overrides without requiring a new prompt.
Can I use custom ComfyUI workflows with this server?
Yes, place JSON workflow files in the workflows/ directory with PARAM_* placeholders to expose parameters as MCP tools.
How are assets identified and referenced?
Assets are identified by (filename, subfolder, type) tuples instead of URLs for robustness across hostname changes.

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Last updated · Auto-generated from public README + GitHub signals.