Slurm

claude mcp add --scope project clio-slurm -- clio-kit mcp-server slurm

Install the launcher first. Restart your client after setup. Configuration locations →

clio-kit doctor --server slurm --connect

Tools​

slurm_submit

Submit one Slurm job or array and return its scheduler-native job ID. Set array only for an array submission.

slurm_list

List a bounded number of Slurm jobs with optional user, state, and partition filters. Returns native IDs and explicit truncation state.

slurm_describe

Describe one scheduler-native Slurm job: lifecycle state, terminality, scheduler properties, and optional bounded stdout/stderr tails.

slurm_cluster

Inspect bounded Slurm partition and queue records in one snapshot. Node details are excluded by default and bounded when requested.

slurm_cancel

Request destructive cancellation of one Slurm job. confirm_job_id must exactly repeat job_id; omission or mismatch is rejected without calling scancel.

Examples​

Replace example paths with your own files and check the returned results.

1. Job Submission and Monitoring​

I need to submit a Python simulation script to Slurm with 16 cores and 32GB memory, then monitor its progress until completion.

Tools called:

• slurm_submit - Submit the job with its resource request
• slurm_describe - Query lifecycle state and scheduler details

2. Array Job Management​

Submit an array job for parameter sweep analysis with 100 tasks, each requiring 4 cores and 8GB memory, then check the overall progress.

Tools called:

• slurm_submit with array - Submit the parallel array
• slurm_list - Find its scheduler-native job ID
• slurm_describe - Query array state and details

3. Job Management and Cleanup​

I have a long-running job that needs to be cancelled, and I want to retrieve the output from a completed job before cleaning up.

Tools called:

• slurm_describe with bounded output - Review job state and logs
• slurm_cancel with exact ID confirmation - Request cancellation

4. Comprehensive Cluster Analysis​

Analyze the current cluster queue status, identify bottlenecks, and suggest optimal resource allocation for my pending jobs.

Tools called:

• slurm_cluster - Inspect partitions, queue state, and capacity
• slurm_list - Review the user's pending jobs and scheduler-native IDs

The default user contract exposes the five tools listed above. Interactive allocation helpers are not part of that contract. Check scheduler access and target partition limits before submitting; local hardware can describe a login node rather than a compute node.

The current submission implementation may emit a plain stdout status message. Check the returned job ID and scheduler result; a successful submit is not a completed workload.