ComfyUI MCP Server Documentation
==================================
Welcome to the ComfyUI MCP Server documentation! This package provides a Model Context Protocol (MCP) server that enables AI agents to manage ComfyUI workflows using a human-readable Domain Specific Language (DSL).
.. toctree::
:maxdepth: 2
:caption: Contents:
quickstart
api_reference
examples
development
Quick Start
-----------
Installation::
pip install comfy-mcp
Basic usage::
from comfy_mcp.mcp.server import mcp
if __name__ == "__main__":
mcp.run()
Key Features
------------
🔄 **DSL-First Design**
Agents work entirely in human-readable DSL, with automatic JSON ↔ DSL conversion.
📁 **File Operations**
Read, write, list, validate, and analyze ComfyUI workflows.
⚡ **Execution Operations**
Execute workflows on ComfyUI servers with real-time monitoring.
🎨 **DSL Syntax**
Intuitive workflow syntax with sections, nodes, and connections.
Architecture
------------
The MCP server exposes 8 core tools:
**File Operations:**
- ``read_workflow`` - Auto-converts JSON to DSL
- ``write_workflow`` - Saves DSL as JSON/DSL
- ``list_workflows`` - Discovers workflow files
- ``validate_workflow`` - DSL syntax validation
- ``get_workflow_info`` - Workflow analysis
**Execution Operations:**
- ``execute_workflow`` - Run DSL workflows on ComfyUI
- ``get_job_status`` - Monitor execution & download images
- ``list_comfyui_queue`` - View ComfyUI queue status
Example DSL Workflow
--------------------
.. code-block:: dsl
## Model Loading
checkpoint: CheckpointLoaderSimple
ckpt_name: sd_xl_base_1.0.safetensors
## Text Conditioning
positive: CLIPTextEncode
text: a beautiful landscape, detailed, photorealistic
clip: @checkpoint.clip
negative: CLIPTextEncode
text: blurry, low quality
clip: @checkpoint.clip
## Generation
latent: EmptyLatentImage
width: 1024
height: 1024
sampler: KSampler
model: @checkpoint.model
positive: @positive.conditioning
negative: @negative.conditioning
latent_image: @latent.latent
seed: 42
steps: 20
## Output
decode: VAEDecode
samples: @sampler.latent
vae: @checkpoint.vae
save: SaveImage
images: @decode.image
filename_prefix: output
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
