Dreamer MCP
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- AlicenseAqualityDmaintenanceEnables comprehensive ComfyUI workflow automation including image generation, workflow management, node discovery, and system monitoring through natural language interactions with local or remote ComfyUI servers.3138 PyPI14MIT
- AlicenseNot gradedqualityNot gradedmaintenanceEnables AI assistants to interact with local ComfyUI installations to list nodes, validate workflows, and execute image generation workflows directly without requiring an HTTP server.1MIT
- FlicenseBqualityDmaintenanceConnects AI assistants to ComfyUI for image, video, and audio generation, providing full control over ComfyUI through 40+ tools including quick generation, cloud API nodes, and custom workflow building.46-
- FlicenseAqualityAmaintenanceExposes 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.10-
- AlicenseBqualityBmaintenanceEnables discovery of ComfyUI nodes and building/managing workflows with real-time execution via WebSocket. Provides 50+ tools for node discovery, workflow building, template usage, model management, and batch/chain execution.6158 npm5MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to generate images using ComfyUI by exposing tools for image generation, batch processing, and status checking through the Model Context Protocol.1MIT
TDQS
Scored across 20 tools
Each tool targets a distinct purpose: workflow introspection, execution, generation, file I/O, and job management are clearly separated. Even overlapping concepts like comfyui_run vs the generate_* wrappers are differentiated by generic vs preset usage. The descriptions reinforce these boundaries, so an agent is unlikely to misselect.
All tools share the comfyui_ prefix and use snake_case, but the pattern mixes verb_noun (generate_image, save_workflow) with noun-only names (status, models, history, queue). This is a minor inconsistency; the prefix and clear resource names keep the convention mostly predictable.
At 20 tools, the server is on the heavy side for an MCP integration, falling into the 16-25 range that feels borderline. The count is justified by the breadth of ComfyUI operations, but several generate_* wrappers plus a generic run tool add redundancy in scope.
The surface covers the core workflow lifecycle: list/inspect, run, save/convert, status/history, outputs, upload, cancel/queue, and model/resource management. Minor gaps like workflow deletion or explicit file download are absent but workarounds exist (e.g., URLs from get_output).