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sciflow-mcp
by Claybarn

validate_workflow

Statically checks a workflow or pipeline for invalid references, cycles, input/output kind and parameter issues, then returns execution order and each step's output kind.

Instructions

Statically check a workflow or pipeline (JSON object, YAML/JSON text, or file path): references, cycles, input/output kinds and parameters. Returns the execution order and each step's output kind; pipelines are compiled first (session steps appear as '__').

Input Schema

NameRequiredDescriptionDefault
workflowYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it discloses what is validated, that execution order and per-step output kinds are returned, and that pipelines are compiled first with session steps renamed to '<session>__<step>'. It omits one important behavior: whether failures raise an error or come back as a validation-result payload.

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

Conciseness5/5

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

Two dense sentences with the core action, the accepted input forms, and the output shape, all front-loaded with zero filler.

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

Completeness4/5

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

An output schema exists, so return format need not be explained, yet the description still summarizes it helpfully. Coverage is strong for a single-parameter validation tool; the only meaningful gap is failure/error reporting semantics.

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

Parameters4/5

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

Schema coverage is 0% and the single anyOf parameter is undocumented structurally, but the description compensates by naming all three accepted forms: a JSON object, YAML/JSON text, or a file path. This directly resolves the anyOf ambiguity the schema leaves open.

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 and resource: "Statically check a workflow or pipeline," and enumerates what is checked (references, cycles, input/output kinds, parameters). The word "statically" implicitly separates it from run_workflow, but no sibling is named 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 context of use is implied by "statically check" (validate before executing), but the description never says when to reach for this versus run_workflow or visualize_workflow. There are no exclusions, prerequisites, or named alternatives.

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