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Connect to NeuralForgeAI to launch, monitor, and manage YOLO training jobs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "wyoloservice2_mcp" yet — see the docs or source repo.
Connect to the NeuralForgeAI cluster, use dataset path /datasets/traffic with the default training configuration, launch a new YOLO training job, and return the job ID and initial status.
Returns the created training job details, including job ID, config summary, and current status.
Check the current status, recent logs, key metrics, and whether YOLO training job job-4821 is still running.
Outputs the job status, latest training logs, metric summary, and any anomaly warnings.
First verify whether dataset path /datasets/old-set is valid; if job job-3907 has failed or is stuck due to data issues, cancel it and explain why.
Returns the dataset path validation result and, if needed, confirms the job was canceled with the reason.
Use natural language to generate images and manage SD Forge NEO models.
Let AI manage Forgejo or Gitea repositories, issues, and pull requests.
Enable AI agents to analyze images with detection and embedding services.
Orchestrate multi-AI workflows with file locking, knowledge capture, and drift detection.
Let AI coding assistants access Yocoolab feedback, selections, and activity context.
Run multiple AI CLIs in parallel with async multitasking and auto permissions.