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ZeroWidth Workbench

Scaffold a first-draft Workbench flow

workbench_flows_scaffold

Creates a runnable first-draft flow from a spec you author: pick the simplest pattern that fits (classifier for read-and-bucket, structurer for transform/extract/draft-for-review, agent for genuinely conversational), write a production-quality system prompt grounded in what the user told you, and mark anything stubbed with [STUB: ...] markers plus stubNotes. The draft opens in Workbench's simple editor at /w//flows/ — give the user that path. May return needs_confirmation; show the user what you're proposing and wait for their approval, then re-call with the approvalId. When the draft comes from a Compass change, pass its opportunityId so the flow and the change point at each other (Compass shows 'Open in Workbench'; the flow shows where it came from). Then: workbench_flows_run to try it, workbench_flows_publish when it's ready for schedules and share links.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesPattern editor the draft opens in. Pick interviewer for conversations that LEARN something from a person (research calls, intake, retros) — pair it with workbench_flows_share_create so non-account humans can talk to it.
modelNoThe model node slug the draft runs on — an `llm` entry from workbench_node_catalog_get that the workspace allows (e.g. an OpenAI, Anthropic, or Google model). Defaults to Claude Haiku 4.5. Set it here rather than editing the flow afterwards.
flowNameYesImperative + specific, ≤80 chars.
stubNotesNoWhat's stubbed / missing / worth wiring next.
workspaceNoWorkspace slug. Personal tokens with no default workspace MUST pass this.
approvalIdNoApproval id from a prior needs_confirmation response. Omit on the first call.
categoriesNoclassifier only: 2-12 buckets.
descriptionNo1-2 sentences: what it does, where it fits.
systemPromptNoThe draft's behavior — production-quality first attempt with [STUB: ...] / [FILL IN: ...] markers where reality is missing. Required for structurer and classifier. Leave it out of an agent to get the bare model with no instructions (a 'promptless' agent).
opportunityIdNoCompass change (from compass_opportunities_list) this draft implements. Sets the flow ↔ change link; refused when the change already has a draft.
inputDescriptionNoWhat the flow's single input carries.
responseSchemaJsonNostructurer only: the output JSON Schema, JSON-encoded as a string (object root, required fields, additionalProperties false).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare the generic safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false). The description goes well beyond that: the draft opens in Workbench's simple editor at a concrete path, may return needs_confirmation requiring user approval and re-call with approvalId, and opportunityId is refused when the change already has a draft. Rich behavioral disclosure the annotations cannot supply.

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?

Front-loaded with the core action and then structured as author-guide → return-path → follow-on tools. Dense and long for a single description, but nearly every clause is actionable and nothing reads as filler.

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?

For a 12-parameter mutation tool with no output schema, the description covers the critical operational context: what gets produced, where it opens, the approval loop, the linking behavior, and the recommended next tools. An agent has enough to call it correctly on the first attempt.

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 100%, so the baseline is 3, but the description adds meaning on top: it explains how to choose `mode` (pattern → use case), calls for [STUB: ...] markers paired with stubNotes, and describes the flow↔change linking role of opportunityId. It adds value beyond the schema, though it does not touch every parameter.

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?

States a specific verb and resource ('Creates a runnable first-draft flow from a spec you author') and frames the scope as scaffolding a draft rather than editing, running, or publishing one. An agent can distinguish it from siblings like workbench_flows_update, workbench_flows_fork, and workbench_flows_run without opening any schema.

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

Usage Guidelines5/5

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

Gives explicit pattern-selection guidance (classifier for read-and-bucket, structurer for transform/extract, agent for conversational) and names the follow-on tools with their conditions: workbench_flows_run to try it, workbench_flows_publish when ready for schedules/share links. It also covers the needs_confirmation edge case and the Compass opportunityId path.

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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