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automatelab-n8n-mcp

Generate an n8n workflow from a description

workflow.generate
Read-only

Convert plain-English workflow descriptions into valid n8n JSON, including triggers, actions, and AI agent configurations.

Instructions

Generate a valid n8n workflow JSON from a plain-English description. Handles webhook/schedule/RSS triggers, common action nodes (Slack, Google Sheets, Discord, Gmail, Notion, HTTP), and AI Agent setups (LangChain root agent + chat model + memory + optional HTTP tool, wired with ai_languageModel / ai_memory / ai_tool connections). Returns workflow JSON with unique node IDs, connections, positions, and typeVersion on every node. Output is non-deterministic (random node IDs and webhook paths).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional workflow name. Derived from the first sentence of the description if omitted.
descriptionYesPlain-English workflow description, e.g. 'Stripe webhook -> Slack message + Google Sheets row'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflowYesFull n8n workflow JSON (name, nodes, connections, settings, ...).
Behavior5/5

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

The description adds behavioral traits beyond annotations: non-deterministic output (random node IDs and webhook paths) and specific handled node types. Annotations already indicate read-only and non-destructive, so no contradiction.

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?

Three sentences front-loaded with the core purpose, followed by capability details and a note on non-determinism. No extraneous content.

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?

Given the complexity of workflow generation, the description covers all essential aspects: input format, supported features, output characteristics, and non-determinism. The existence of an output schema reduces the need to detail return values, making this complete.

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% and the description adds value by providing an example input for the 'description' parameter and clarifying that 'name' is derived from the first sentence if omitted. This enhances understanding beyond the raw schema.

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 'Generate a valid n8n workflow JSON from a plain-English description' and enumerates supported triggers, action nodes, and AI Agent setups, distinguishing it from sibling tools like workflow.create or workflow.lint.

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?

The description implies usage for converting plain-English to workflow JSON, but it does not explicitly contrast with sibling tools like workflow.create or provide when-not scenarios. Still, the purpose is clear enough for most agents.

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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