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Manage SLURM clusters over SSH for jobs, monitoring, queues, and files.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-slurm" yet — see the docs or source repo.
Connect to my SLURM cluster, submit the training job using /home/user/train.sbatch, and return the job ID, partition, expected resource usage, and a submission summary.
Returns whether submission succeeded, the job ID, target partition, and requested resources.
Check the current user's job queue, list running, pending, and failed jobs, and summarize resource usage across key nodes.
Outputs a queue status list and an overview of node CPU, memory, and other resource usage.
Find the stdout and stderr logs for job 123456, extract the key error snippets, and list the related output file paths.
Returns log locations, a summary of key errors, and a list of related files.
Connect to Slurm clusters to inspect jobs, queues, and HPC scheduling tasks.
Monitor and manage Slurm GPU jobs, allocations, and logs across clusters.
Remotely manage Linux servers via SSH, commands, Docker, files, and code deployment.
Let AI handle secure remote SSH commands, transfers, and port forwarding.
Execute SSH commands on remote servers and resume large file transfers.
Let AI assistants read, search, and interact with Slack workspaces.