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design_workflow

Designs multi-step workflows from natural language descriptions with constraints, producing a draft for review and iterative refinement.

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:

  1. AI calls design_workflow(goal="...") → returns draft with proposed steps

  2. User reviews: "step 3 should use vm_power_off instead" or "add an approval before step 4"

  3. AI calls update_draft(workflow_id, ...) to modify

  4. User confirms: "looks good"

  5. AI calls confirm_draft(workflow_id) → state changes to PENDING

  6. AI calls run_workflow(workflow_id) → execute

The AI should use get_skill_catalog() first to understand available tools, then propose steps based on the user's goal.

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?

Annotations indicate readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true. Description adds that it returns a DRAFT workflow, outlines the interaction flow, and explains that the state becomes DRAFT. No contradiction with annotations.

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?

Description is well-structured with a clear tag, numbered flow, and separate args/returns sections. Slightly lengthy but each sentence adds value and it is front-loaded with purpose.

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?

Covers purpose, when to use, design flow, parameter details, return structure (workflow_id, state, placeholder), and prerequisite use of get_skill_catalog(). No output schema but description provides sufficient context for a complex tool.

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 has 0% description coverage; description explains 'goal' as natural language description and 'constraints' with examples. This adds meaning beyond schema titles and types, compensating for lack of schema descriptions.

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 it is for designing a workflow from a natural language description, with specific verb 'design' and resource 'workflow'. It distinguishes from siblings like 'plan_workflow' by outlining the draft-review-confirm-run flow.

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?

Explicitly says 'Call this when the user describes a complex operation and you need to design a multi-step workflow.' Provides a numbered design flow and mentions using get_skill_catalog() first. Does not explicitly state when not to use, but context is clear.

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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