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Fork a public flow into the workspace

workbench_flows_fork

Copies a PUBLIC flow from another workspace (a template) into this one as a new draft the user owns. Identify it by its flowUuid (the id on Workbench's public template pages and in workbench_flows_get output); optionally pin which published version to copy. Flows already in this workspace can't be forked — open them instead. Counts toward the plan's flow cap. May return needs_confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowUuidYesThe source flow's public uuid.
workspaceNoWorkspace slug. Personal tokens with no default workspace MUST pass this; tokens with a default can override per call. Ignored for workspace API keys.
approvalIdNoApproval id from a prior needs_confirmation response. Omit on the first call.
forkedFromVersionNoPublished version label to copy. Default: the latest published.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare the mutation profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false). The description adds real context beyond them: the result is a user-owned draft, it counts toward the plan's flow cap, and it may return `needs_confirmation`. It stops short of describing the response shape or how to resolve the confirmation flow in detail.

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?

Four tight sentences, front-loaded with the core action and constraints. Every sentence carries distinct information (identity, version pinning, non-forkable case, cap cost, confirmation signal) with no 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 4-param mutation with no output schema, the description covers the critical agent-facing facts: public-source requirement, version selection, the already-in-workspace exclusion, quota impact, and the confirmation return. Nothing essential to calling it correctly is missing.

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 locating value for flowUuid ('the id on Workbench's public template pages and in `workbench_flows_get` output') and clarifies that forkedFromVersion optionally pins a published version. It goes beyond restating the schema fields.

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+resource ('Copies a PUBLIC flow from another workspace into this one as a new draft') and immediately clarifies the fork semantics (template -> owned draft). It distinguishes itself from siblings by noting flows already in the workspace can't be forked and should be opened instead, and by pointing to workbench_flows_get for the id.

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?

Explicit when-to-use (a PUBLIC/template flow from another workspace), explicit when-not ('Flows already in this workspace can't be forked — open them instead'), and it names the alternative path. Nothing about selection is left to inference.

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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