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design_workflow

Designs a multi-step workflow draft from a natural language goal, enabling review and editing before execution.

Instructions

[WRITE] Start designing a workflow from a natural language description.

Call this when the user describes a complex operation and you need to design a multi-step workflow. Returns a DRAFT workflow with proposed steps for the user to review and edit before execution.

Design flow: design_workflow → update_draft (add steps, then iterate on user feedback) → confirm_draft (state becomes PENDING) → run_workflow.

Use get_skill_catalog() first to see which tools the steps may target.

Args: goal: Natural language description of what the user wants to accomplish. constraints: Optional constraints (e.g. "must have approval before any destructive step", "use NSX for networking", "target is vcenter-prod").

Returns: dict with workflow_id (state=DRAFT), proposed steps placeholder, and instructions for the AI to fill in steps via update_draft.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYes
constraintsNo
Behavior4/5

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

The description goes beyond annotations by clarifying that the tool creates a DRAFT workflow with 'proposed steps placeholder,' meaning the output is incomplete and requires further updates via update_draft. It also states the workflow state is DRAFT. This adds valuable behavioral context that the annotations (readOnlyHint=false, openWorldHint=true) do not convey. No contradictions.

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?

The description is well-structured with a clear opening, usage context, flow, prerequisites, args, and returns. Each section adds necessary information; nothing seems redundant.

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

Completeness5/5

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

The description explains return values (workflow_id, state=DRAFT, placeholder, instructions) despite no output schema. It also outlines the full design flow and prerequisites, making it complete for the tool's complexity.

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?

The input schema provides no parameter descriptions (0% coverage), but the description fills the gap with clear definitions for both 'goal' and 'constraints,' including examples for constraints. This fully compensates for the schema gap.

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 the tool's function: 'Start designing a workflow from a natural language description.' It specifies the resource (workflow) and the action (design), and distinguishes it from siblings by positioning it as the initial entry point in the design flow (design_workflow → update_draft → confirm_draft → run_workflow).

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

Usage Guidelines4/5

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

Provides explicit when-to-use: 'Call this when the user describes a complex operation and you need to design a multi-step workflow.' It also instructs to use get_skill_catalog() first. However, it does not explicitly mention when not to use it or alternative tools like create_workflow, so a 4.

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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