comfygenmcp
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Related Servers
- AlicenseAqualityAmaintenanceAn MCP server that integrates with a local ComfyUI, allowing assistants to list and run workflows, monitor progress, and view results, as well as read and edit the live workflow canvas via an optional bridge node.6411GPL 3.0
- FlicenseNot gradedqualityBmaintenanceMCP server that exposes ComfyUI image and 3D generation as a single tool with smart prompt classification. It enables AI assistants to generate assets directly by selecting and running the appropriate ComfyUI workflow.-
- FlicenseAqualityDmaintenanceAn MCP server for generating images and checking generation status through ComfyUI's API. It allows interaction with local ComfyUI instances by providing workflow-based image generation and status checking tools.3-
- FlicenseNot gradedqualityBmaintenanceMCP server that connects local ComfyUI to AI agents, enabling natural language control of ComfyUI for creating workflows, generating images, and managing the queue.-
- FlicenseNot gradedqualityCmaintenanceMCP server exposing a local (or LAN) ComfyUI instance's HTTP API as tools, so an LLM client can queue generations, inspect the queue/history, upload reference images, browse installed models/nodes, and pull back generated images.-
- AlicenseNot gradedqualityDmaintenanceMCP server that enables AI agents to control a local ComfyUI instance for image generation, allowing workflow understanding, parameter modification, execution, and model discovery.23 npm3Apache 2.0
TDQS
Scored across 2 tools
list_workflows and generate_image are completely distinct: one discovers available workflows, the other executes generation. There is no possibility of confusing their roles.
Both tool names follow the same verb_noun pattern (list_workflows, generate_image), making the interface predictable and consistent.
At only 2 tools, the server feels thin, but the narrow scope of listing and generating ComfyUI workflows makes this minimal surface reasonable. Still, it is borderline per the calibration guideline.
The core workflow is complete: list ready workflows and generate images from them. Minor gaps exist, such as no workflow detail endpoint or explicit handling of input-image workflows, but these are edge cases rather than dead ends.