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.3120 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-
- AlicenseNot gradedqualityDmaintenanceEnables 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.85 npm3MIT
- 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.MIT
- AlicenseNot gradedqualityNot gradedmaintenanceEnables AI agents to generate and iteratively refine images, audio, and video by interacting with a local ComfyUI instance through natural conversation. It provides comprehensive tools for workflow management, node introspection, and publishing generated assets.Apache 2.0
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).