# N8N2MCP — Open Source | Super Cat

> Turns an n8n workflow into a Model Context Protocol server, so automation a team already built becomes a tool Claude or Cursor can call directly.

Source: https://www.super-cat.tech/portfolio/n8n2mcp  
Last modified: 2026-10-22

[← All case studies](https://www.super-cat.tech/portfolio)Nearly 130 GitHub stars · MIT licensed

# N8N2MCP — Open Source

Turns an n8n workflow into a Model Context Protocol server, so automation a team already built becomes a tool Claude or Cursor can call directly.

## The problem

Teams adopting the Model Context Protocol almost always start by writing new tools from scratch — rebuilding integrations they already have running somewhere else.

## What we built

N8N2MCP converts an n8n workflow into an MCP server, so automation a team already built becomes a tool that Claude, Cursor or any MCP client can call directly. Paste the workflow, map the credentials, deploy, and the endpoint is live. Open source, MIT licensed.

Before: Teams rebuild tools they already have; Integrations duplicated for MCP. After: Nearly 130 GitHub stars · MIT licensed.Before

- Teams rebuild tools they already have

- Integrations duplicated for MCP

How it works

- 01 Paste the workflow. An existing n8n workflow is the input; nothing is rewritten.

- 02 Map the credentials. The workflow’s credentials are mapped to the new server.

- 03 Deploy as an MCP server. The workflow is exposed as a Model Context Protocol tool.

- 04 Call it from any client. Claude, Cursor or any MCP client calls it directly.

After
Nearly 130 GitHub stars · MIT licensed

Stack
Open Source MCP n8n Claude

## Why it holds up

The tools are usually already there, sitting in the workflow engine, one adapter away from being callable. Nearly 130 GitHub stars is third-party validation of that argument that nobody paid for — anyone can read the source and check the claim.

Related service: [AI staff augmentation](https://www.super-cat.tech/ai-staff-augmentation)

## More case studies

Big 4 client · fully on-premise

### Enterprise RAG Chatbot for a Big 4 Accounting Firm

Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached.

- Juniors reading thousands of pages per question

- Seniors acting as search engines

Read the case → 200k calls/month · 67% booking rate vs 77% human

### 24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands

Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot.

- Night and weekend calls unanswered

- Staffing every shift hard to justify

Read the case → US$1M collected in the first 3 months

### AI Payment Collection and Reminders

The companion product to the AI front desk: it chases unpaid invoices with polite, timed reminders and a payment link, and stops the moment the customer pays or replies. It collected over US$1 million for the client in its first three months.

- Chasing payment is awkward and late

- Billable time lost to collections

Read the case →

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## Talk to the engineer who will build it.

You will speak with me directly. Bring the use case, the data and the constraints, and I will give you a straight view on feasibility, effort and the first thing to ship.

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