comfy-workflow-mcp
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- AlicenseNot gradedqualityDmaintenanceDynamically loads ComfyUI workflows as MCP tools, enabling AI assistants to generate images, videos, and audio by executing workflows across categories like text-to-image, image-to-video, and text-to-audio with automatic parameter mapping and progress monitoring.12,397 npm3MIT
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TDQS
Scored across 10 tools
Most tools have distinct targets, but two clear pairs shade into each other: run_workflow vs run_judged and get_job vs get_judged. The descriptions do explain the pairing (get_job polls run_workflow, get_judged polls run_judged) and judge_image vs run_judged is clarified as single-image judging versus an iterative harness, so a careful agent can pick correctly, but misselection risk remains for an inattentive one.
The dominant pattern is verb_noun (list_workflows, describe_workflow, run_workflow, judge_image, get_job, upload_image, list_models). Deviations are minor: server_status is noun_phrase rather than verb_noun, and run_judged/get_judged use a past-participle object instead of a noun, which slightly muddies the run/poll pairing.
Ten tools sit squarely in the well-scoped range, and each earns its place: discovery (server_status, list_workflows, describe_workflow, list_models), execution (run_workflow, upload_image), polling (get_job), and the judging layer (judge_image, run_judged, get_judged). No obvious filler or redundant tool.
The lifecycle is largely covered: inspect server, discover/describe workflows, upload inputs, run, poll results, and iterate with a judge. The notable gap is queue control — there is no cancel/interrupt for a running job or a way to clear the ComfyUI queue, which agents will need when a run hangs or a seed sweep should stop.