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ComfyUI MCP

ComfyUI MCP

by artokun
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

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Alternatives to ComfyUI MCP

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  • comfy-workflow-mcp

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    Exposes the workflows saved in a ComfyUI server (local, remote, or vast.ai) as MCP tools, letting agents list them, inspect their inputs, and run them with their own prompts, images, seeds, and per-run node toggles. It also reports server/GPU status, tracks and downloads job outputs, and uploads images or browses model folders.
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Deploy Server
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TDQS

A3.8/5.0

Scored across 41 tools

Disambiguation3/5

Each tool has long, precise descriptions and almost every multi-mode tool uses action subcommands, so individual tools are rarely ambiguous. However several cross-tool boundaries blur: top-level get_workflow vs queue action:'get_workflow', multiple model download paths (download_model, comfy_cli models_download, apply_manifest), and multiple enqueue surfaces. An agent would need to read descriptions carefully to pick the right one, so overlap exists but can be resolved.

Naming Consistency3/5

The dominant pattern is snake_case verb_noun (get_workflow, save_workflow, generate_image, install_custom_node), which is good. But there are also bare-noun tools (queue, runpod, workspace, apps, batch, kitchen, bisect), a noun_verb outlier (runpod_watch), and an inconsistent train_* cluster (train_prepare_dataset, train_start, train_doctor). This is readable but not a single predictable convention.

Tool Count2/5

Forty-one top-level tools, each carrying multiple action subcommands, makes this a very heavy surface for an agent to navigate. While the domain is broad (ComfyUI, model training, RunPod, custom-node development), several tools could be consolidated (runpod/runpod_watch, queue/enqueue_workflow, list_packs/list_tools). It exceeds the reasonable 25+ threshold on the scale.

Completeness4/5

The server covers the ComfyUI lifecycle unusually well: workflow creation/validation/submission, queue/history management, model discovery/installation/deletion, custom-node management, training, RunPod control, and asset retrieval. Gaps are minor, such as no delete/rename for saved workflows and no direct cleanup of generated assets. Overall, agents can complete most user workflows without hitting dead ends.

Maintenance

ActivityActive
ResponsivenessResponsive

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