Exploring AI Agent Possibilities with MCP and n8n
Making use of Anthropic's new Model Context Protocol
I’m a Product and Operations minded person with a background in low-code automation tools like Tray.io and Workato. I’ve recently been fascinated by the potential of AI-Agent-driven workflows. I’ve been thinking about this for two different purposes:
For powering AI native Products with Agent functionality for external customers. I also see this as learning an aspect of the technology that is predicted to disrupt some of our current SaaS tools.
For automating back of the house business workflows that could make any business more efficient.
Recently, I’ve set out to get my hands dirty with a new challenge: building an Agent setup that combines the Model Context Protocol (MCP) and n8n, a popular open-source workflow automation platform. Here’s how I approached it, what I learned, and why this AI Agent approach could likely satisfy some product functionality or business automation needs.
Why MCP and n8n?
Before diving into the setup, let’s define the key concepts:
n8n: An open-source platform for building workflows, often hailed as a go-to standard for low-code AI Agent automation. I like that you can bypass paying for it as a SaaS tool if you self-host it yourself (you still have the hosting fees though!). The community support for it is also quite large at the moment.
Model Context Protocol (MCP): Developed by Anthropic, MCP is an open-source protocol that allows AI systems (like Claude, ChatGPT, or others) to securely connect to external data sources, tools, and prompts via a client-server architecture. It is sort of like a universal API for AI Agents. (Learn more: Anthropic’s announcement and MCP’s intro).
Together, they seemed like a great combo for learning some functional aspects of AI Agents.
Setting It Up
Here’s how I got started:
Self-Hosting n8n:
To unlock the full potential of this setup, you need to self-host n8n. Why? Because the magic happens with a community “node” (or workflow step) that isn’t available in the cloud version. I used Elest.io for hosting because the setup was easy for n8n. That said, you could opt for Digital Ocean, Hostinger, or any provider that suits your technical comfort level. These other options will most likely be cheaper to host as well.Installing the MCP Community Node:
To bring MCP into n8n, I installed the community node from nerding-io/n8n-nodes-mcp. This node acts as the bridge between n8n workflows and MCP-compatible tools. Without it, you’re only able to use the standard n8n node functionality.MCP Tool Compatibility:
MCP isn’t a free-for-all yet. It only works with tools that have MCP servers implemented. You can check the current list here: modelcontextprotocol/servers.
The Build
The build itself included the following workflow components:
The initial trigger:
The workflow starts when a chat message is received, prompting the LLM-powered Agent to act.
AI Agent node:
The AI Agent node in n8n is the central hub that manages:
The LLM Model: This powers the agent’s conversational ability. I used ChatGPT.
The Memory (Simple Memory Database): This keeps track of past interactions for context-aware LLM responses.
The Tools: This is where the MCP connections were.
MCP List Tools (Brave Search):
This node lets the agent discover available MCP tools. In this example, it’s the Brave Search tool for web queries. This is one of the unique aspects of MCP as opposed to other agent-building approaches. The tool makers can tell the LLMs powering an Agent which functionality is available.
MCP Execute Tool (Brave Search):
Once identified, this node enables the agent to run a Brave Search query and return results to the user.
How It Works
A chat message triggers the AI Agent node, which processes the input using the OpenAI model and memory for context. If needed, it calls Brave Search to list tool options or execute a search. It then responds back to the user. This is all within a compact workflow.
Key Lessons from the Build
As I pieced this together, a few insights stood out:
Prompting Is Critical:
When setting up the Agent Node in n8n, it is extremely helpful to include a system prompt instructing it to use tools. Skip this, and your workflow will ping-pong inefficiently, wasting time and resources. A clear prompt is your guardrail to this. This is also just good standard advice anytime you are developing an Agent step utilizing LLMs.MCP’s Potential as the “API” for AI Agents:
Official MCP integrations are still limited, but its design feels like the most likely future. For product managers, operations, or anyone building AI-driven solutions, learning MCP now could provide an advantage. It’s poised to become a universal standard for agent-tool interactions.Caveat: Tool Provider Dependency:
Here’s the catch; MCP’s functionality hinges on the tool providers maintaining their servers. If a provider updates or breaks something, those changes flow straight into your workflow. It’s a double-edged sword: provider managed updates, but less control for you. This may become a consideration in the same way that package dependencies plague development teams. I wouldn’t be surprised to see a whole application security sub-discipline arise from this as well.
Why This Matters
What excites me most about this setup is the promise of its flexibility in the future. With MCP, my Agent can tap into a growing ecosystem of tools without reinventing the wheel for each integration. Pair that with n8n’s workflow builder, and you’ve got a low-code playground for experimenting with AI agents. I’ll be honest though, it was definitely still a little clunky since I implemented this with a newly developed community package in n8n that’s not yet natively supported. Limited tool options on the third-party MCP servers is also a hurdle at the moment.
For anyone curious about the intersection of AI and automation, MCP is definitely worth a look. It might make sense to hold off on implementing it within n8n for a little bit though if you’re looking to build a production workflow, since the support is rapidly evolving. I would definitely recommend testing playing around with it in n8n or another workflow in order to learn the ropes though.
Thanks for reading!