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Step by Step Creation of MCP Server and integration with n8n
3 students

Step by Step Creation of MCP Server and integration with n8n

Step by Step Creation of MCP Server and integration with n8n
Created byBhoopesh Sharma
Last updated 12/2025
English

What you'll learn

  • Grafana Observability
  • OpenSource Observability
  • MCP Server Understanding
  • n8n Workflow Automation

Course content

1 section5 lectures34m total length
  • AI Monitoring Agent with MCP and n8n -> Part 110:00
  • AI Monitoring Agent with MCP and n8n - Part 25:47
  • AI Monitoring Agent with MCP and n8n - Part 34:06
  • Grafana and Prometheus MCP Server via Python - Part110:00
  • Grafana and Prometheus MCP Server via Python - Part24:43

Requirements

  • Nothing specific other than basic Python

Description

Create an MCP server and integrate it with n8n in steps:

  1. Initialize project: create a new Python virtualenv, install required packages (example: mcp-server, requests, fastapi or FastMCP).

  2. Implement server: write a small FastMCP-based app that exposes REST endpoints to read and expose metrics or accept control commands. Include configuration variables for target Prometheus URL and Grafana API key.

  3. Run locally: start the MCP server with uvicorn or the provided runner, verify endpoints with curl on localhost.

  4. Containerize (optional): add a Dockerfile, build and push to local registry or use docker-compose with ports and environment variables.

  5. Prepare n8n: run n8n in Docker or locally and open the editor.

  6. Create workflow: add a Webhook or Schedule trigger node to start automation. Use an HTTP Request node to call the MCP server endpoints (GET for metrics, POST for actions). Add Function or Set nodes to transform payloads.

  7. Add remediation: use SSH, Execute Command, or Docker nodes to restart services based on MCP responses. Secure calls with API keys in n8n credentials.

  8. Test and monitor: trigger the workflow, inspect execution, add retries and logging.

This integration enables automated observability-driven remediation and easy orchestration between monitoring automation.

This Practical example will help in understanding the usecase

Who this course is for:

  • Beginner Python Developers curious on Agentic AI and n8n Workflows