n8n vs. MCP for LLM Workflows

July 06, 2025

Building AI-powered workflows comes with many options, so it can feel overwhelming. The choice might come down to choosing something like n8n, which offers drag-and-drop automation and easily integrates with other tools, or MCP (Model Context Protocol), a standard for defining how LLMs interact with tools and data.

Both approaches have their strengths. Depending on what you’re trying to build, one might save you weeks of engineering time.

To understand this better, I’m breaking down where each one excels and struggles, and how to decide which one is the best fit for your project.

What is n8n?

n8n is an open-source workflow automation platform that helps you connect apps without writing much code. It’s similar to Make, but is self-hosted and highly customizable.

Over the past year, n8n has steadily expanded into AI territory. You’ll now find built-in nodes for:

• Chat completion with OpenAI or Anthropic
• Embeddings generation
• Summarization pipelines
• Custom function nodes to process LLM output

This makes n8n very popular amond developers who want to:

• Prototype AI workflows quickly
• Automate marketing tasks
• Orchestrate LLM calls alongside other SaaS tools like Slack or Airtable

If you’re building an app that needs to move data between systems and it employs AI along the way, n8n would seem to be the fastest route to getting to proof of concept.

What is MCP?

I was really enamored with Model Context Protocol (MCP) earlier this year. MCP is highly structured approach to defining how an LLM interacts with outside tools. It’s not focused on workflows. Instead it sets the rules for:

• The capabilities of a model (“what tools can it call?”)
• The schema of each tool input and output
• How the model can reason about when to use which tool

MCP sits closer to the agent end of the spectrum. It’s built to help developers build sophisticated AI assistants. You’ll see MPC being used when there is a need to:

• Select and invoke tools dynamically
• Handle multi-step reasoning
• Keep track of context across multiple user queries

MCP gains attention among any team working on LLM orchestration frameworks, such as those inspired by LangChain. If you want to build an AI agent that behaves consistently over time, MCP provides is your tool. It provides what a typical workflow automation tool cannot.

Where n8n works best

n8n will meet your needs if you:

• Need to prototype quickly without concerns about infrastructure
• Want something visual (diagrams) for non-developers
• Are focused on practical automation (like notifications) rather than complex reasoning -Seek to integrate dozens of APIs out of the box

For example, if you want to set up an LLM to summarize support tickets from Zendesk and send them to Slack every morning, n8n is yout tool

Where MCP excels

MCP starts to make more sense when you:

• Are building multi-step, context-aware agents
• Need to enforce strict contracts around tool calls
• Care about making your model outputs more predictable
• Building production-grade workflows that require consistent reasoning

For instance, if you’re designing a financial assistant that can:

• Retrieve account balances
• Run projections
• Compose a detailed response
• Track all steps in a traceable way
• This is where MCP’s structured definitions can help you avoid bad logic and guarantee your models behave reliably.

Should You Pick One Over the Other?

In practice, these tools serve different audiences.

If you want LLM-powered automations that can be deployed quickly and evolve over time with minimal friction, n8n is compelling. It’s practical and easy to share across your team.

If you’re thinking about LLM agents that need careful control, tool selection, and traceability, MCP is a better bet.

Last words

If you’re trying to stand out in the AI space, consider reviewing both. But if you can only go deep on one, n8n is the safer bet for visibility.

The reason is simple. Companies love practical automations they can adopt immediately, and n8n is able to deliver them.

MCP is still important, especially for teams that need to build enterprise-grade assistants. But the adoption curve has not hit the same stride.

Bottom line:

If you want to showcase practical LLM workflows and help others ship them, start with n8n. You can always layer in MCP later as your projects get more complex.