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submit_job

Submit a batch job to a SLURM cluster with configurable compute resources, environment, and dependencies.

Instructions

Submit a SLURM job.

Args:
    command: Shell command to execute (e.g. "python train.py --epochs 100")
    name: Job name for identification in SLURM queue
    nodes: Number of compute nodes to allocate
    gpus_per_node: Number of GPUs per node (0 for CPU-only)
    ntasks_per_node: Number of tasks per node
    cpus_per_task: Number of CPUs per task
    memory_per_node: Memory per node (e.g. "32GB", "64G")
    time_limit: Wall time limit (e.g. "4:00:00", "1-00:00:00")
    partition: SLURM partition name (e.g. "gpu", "cpu")
    nodelist: Specific nodes to use (e.g. "node001,node002")
    conda: Conda environment name to activate before running
    venv: Path to Python virtual environment to activate
    env_vars: Additional environment variables as key-value pairs
    log_dir: Directory for stdout/stderr log files
    work_dir: Working directory for the job (defaults to cwd)
    use_ssh: If true, submit via SSH to remote SLURM cluster

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYes
nameNojob
nodesNo
gpus_per_nodeNo
ntasks_per_nodeNo
cpus_per_taskNo
memory_per_nodeNo
time_limitNo
partitionNo
nodelistNo
condaNo
venvNo
env_varsNo
log_dirNologs
work_dirNo
use_sshNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility. It explains parameters but omits post-submission behavior (e.g., returns job ID? queues? side effects?), leaving important behavioral aspects ambiguous for an agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured as a list of parameter explanations, each concise. It is slightly lengthy but every line adds value. No wasted words, though could group related params for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given high complexity (16 params) and no annotations, the description covers all parameters adequately. However, it lacks context on return values (despite output schema existing), error handling, and typical submission workflow, making it only partially complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates fully by explaining each of the 16 parameters with examples and purpose (e.g., 'Wall time limit (e.g. "4:00:00")'). This adds essential meaning beyond the schema's type information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Submit a SLURM job' and enumerates all parameters with specific roles. It distinguishes itself from siblings like cancel_job, list_jobs, and run_workflow by focusing on job submission.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide explicit guidance on when to use this tool versus alternatives like run_workflow, nor does it mention prerequisites or when not to use it. It is adequate but lacks comparative context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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