Install ComfyUI into a specified local directory with a dedicated virtualenv, installing Python dependencies via pip or uv and optionally ComfyUI-Manager.
MIT
337,950 tools. Last updated 2026-07-30 03:22
"Python context management and dependency solving in ComfyUI" matching MCP tools:
- Starts ComfyUI using saved process info from a prior stop, supporting Desktop and manual Python installs, and polls API until ready.MIT
- Updates the ComfyUI core by pulling latest changes and reinstalling Python dependencies. Requires a local install; errors on remote instances.MIT
- Retrieve ComfyUI environment details: system stats (OS, Python, ComfyUI version, GPU/VRAM) and local probes (git, manager version, torch/CUDA).MIT
- Repairs a ComfyUI custom node pack's installation and Python dependencies, or fixes all packs at once using comfy-cli or Manager HTTP.MIT
- Start, stop, or restart a local ComfyUI server using official comfy-cli background process management.MIT
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- Retrieve ComfyUI server runtime logs to debug execution errors, model loading issues, missing nodes, and Python tracebacks. Filter by keyword and adjust log line count.MIT
- Reconcile Python dependencies for all installed custom node packs in ComfyUI by executing the official restore-dependencies command.MIT
- Validate Python script metadata and dependencies without execution. Checks PEP 723 syntax, dependency format, and requires-python compatibility.MIT
- Retrieve ComfyUI system information including GPU details, VRAM usage, Python version, and operating system. Useful for diagnosing hardware and software configuration.MIT
- Restart ComfyUI using saved process info from a prior stop. Works for Desktop app and manual Python installs, with up to 60 seconds wait for API readiness.MIT
- Detect upward dependency violations in Python codebases by analyzing layer imports. Returns categorized file counts and dependency edges.MIT
- Takes a Python file and specified function/class names, returns their full source code along with compressed dependency context.MIT
- Execute Python code directly in a Jupyter kernel, bypassing notebook context for fast execution and testing.MIT
- Retrieve ComfyUI server runtime logs to debug execution errors, model loading issues, missing nodes, or Python tracebacks.MIT
- Retrieve Python version, device type, and VRAM usage from ComfyUI's system stats endpoint.
- Retrieve system information from a running ComfyUI server, including GPU details, VRAM usage, and software versions. Use to confirm server connectivity and verify available VRAM before running large workflows.MIT
- Retrieve the current ComfyUI execution queue, listing running and pending jobs with prompt IDs and positions. Optionally include workflow payloads.MIT
- Connect to a RunPod pod's ComfyUI instance using its pod ID, so all other ComfyUI tools target that pod.MIT
- Save a workflow JSON to the ComfyUI server's user library, automatically converting API format to Web UI layout so it opens in the canvas. Overwrites existing files without confirmation.MIT