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Lets AI assistants manage distributed jobs and monitor execution on Joblet.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Joblet MCP Server" yet — see the docs or source repo.
Use the Joblet MCP to create a distributed training job with 4 nodes, 2 GPUs per node, image myrepo/trainer:latest, and command python train.py --epochs 10. Return the job ID and a summary of the resource configuration.
Returns the new job ID plus a summary of nodes, GPUs, image, and startup command.
Use the Joblet MCP to check the current status of job-4821, summarize recent failures or retries, and extract key errors from the latest 100 log lines.
Returns the job status, a failure/retry summary, and key exception details from the logs.
Use the Joblet MCP to prepare resources for a data pipeline: mount object storage bucket-a, connect the internal network research-net, and orchestrate preprocessing, training, and evaluation jobs in sequence. Then provide a workflow overview.
Returns storage and network setup results, task dependencies across stages, and a complete workflow summary.
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