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sciflow-mcp
by Claybarn

export_workflow

Compile a workflow or pipeline into Slurm or Argo cluster jobs that run each step against a shared store, coordinating parallel sessions and aggregate steps.

Instructions

Compile a workflow or pipeline into cluster jobs that each run one step against a shared store (a pipeline's sessions run in parallel; its aggregate steps wait for all of them).

slurm options: store_path, partition, time, mem, cpus, account, setup (shell lines, e.g. module load), sciflow_cmd, extra_sbatch. argo options: image, namespace, store_pvc, cpu, memory.

Input Schema

NameRequiredDescriptionDefault
targetYes
optionsNo
out_dirYes
workflowYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Without annotations, the description usefully discloses execution semantics: each job runs one step against a shared store, pipeline sessions run in parallel, and aggregate steps wait. However, it does not clarify whether this only generates job files or actually submits jobs to the cluster, nor does it mention write side effects, permissions, or failure behavior.

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 purpose and behavioral context are front-loaded, and the per-target option lists are terse. The text is dense but every sentence contributes, though the abrupt switch into option lists could be slightly better signposted.

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?

An output schema exists, so return values needn't be explained. But for a 4-parameter tool with no annotations and 0% schema description coverage, the description omits top-level parameter meanings and whether the tool submits cluster jobs or only emits job files, which are important for correct invocation.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must carry parameter meaning. It helpfully enumerates target-specific `options` keys for slurm and argo, but never explains what `workflow` (object vs string), `target`, or `out_dir` mean, leaving several required parameters undocumented.

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

Purpose4/5

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

States a specific verb ('Compile') and resource ('workflow or pipeline into cluster jobs'), making it clear this is a compilation/export operation rather than execution (run_workflow) or validation (validate_workflow). The purpose is distinct from siblings, though it does not name an alternative explicitly.

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 implies usage when preparing a workflow for a Slurm or Argo cluster, but never states when an agent should choose this over run_workflow, validate_workflow, or export_artifact. No explicit exclusions or prerequisites are given.

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