Skip to main content
Glama
AutomateLab-tech

automatelab-n8n-mcp

Semantic diff between two n8n workflows

workflow.diff
Read-onlyIdempotent

Identify differences between two n8n workflows: report added, removed, or modified nodes, connection topology changes, and settings drift, while ignoring irrelevant noise like position shifts.

Instructions

Semantic diff between two workflows. Reports nodes added / removed / modified (with field-level deltas: type, typeVersion, parameters, credentials, disabled, position), connection topology changes, and settings drift. Ignores noise (small position deltas, createdAt/updatedAt). Pair with workflow.get to compare deployed vs local. Deterministic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterYesThe 'after' workflow JSON.
beforeYesThe 'before' workflow JSON.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
changesYesOrdered list of semantic differences.
summaryYesOne-line summary of change counts by kind.
change_countYes
Behavior5/5

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

The description adds significant behavioral context beyond the annotations: it states the tool is deterministic, lists the specific node fields compared, mentions connection topology and settings drift, and explains noise filtering. Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description complements them well with 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 extremely concise: three short sentences plus a pairing recommendation. It front-loads the core purpose, then details output specifics, noise handling, and a use-case suggestion. Every sentence adds value with no redundancy.

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 that the tool has a high schema coverage (100% for parameters) and an output schema exists, the description is sufficiently complete. It covers purpose, detailed behavior, noise filtering, and a concrete use case. No additional information is needed for the agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: both parameters ('before' and 'after') have descriptions stating they are workflow JSONs. The tool description does not add additional parameter-specific semantics beyond what the schema provides, so baseline score of 3 is appropriate.

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 and specifically states the tool's purpose: semantic diff between two n8n workflows. It lists exactly what changes are reported (nodes added/removed/modified with field-level deltas, connection topology changes, settings drift) and what is ignored (noise). This distinguishes it from sibling tools like workflow.get, workflow.lint, and workflow.generate.

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 provides explicit usage guidance by suggesting to pair with workflow.get to compare deployed vs local workflows, which helps agents understand when to use this tool. While it does not explicitly state when not to use alternatives, the context from sibling tool names implies appropriate scoping.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AutomateLab-tech/n8n-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server