When I first heard about MCP (Model Context Protocol), I genuinely thought:

“Oh, this is probably like n8n — some open-source automation tool.”

Turns out, it’s not.

A lot of devs — especially those already familiar with tools like n8n, Zapier, or Make — might assume MCP is just another automation framework. But the truth is, they solve very different problems in very different ways.

So let’s unpack what makes MCP and n8n fundamentally different, and why it's crucial to choose the right one depending on your use case.

✅ What is MCP (Model Context Protocol)?

MCP is a framework and protocol for orchestrating AI agents, especially those built with large language models (LLMs) like GPT-4.

Think of it as a way to structure how your AI agent interacts with tools, data, and prompts — all via a well-defined JSON or GraphQL schema.

🔹 Core Components of MCP:

• Tools: Functions the AI can call (e.g., getUserData, createInvoice, summarizePDF)
• Resources: External data sources the AI can access (e.g., Firestore, Redis, REST APIs)
• Prompts: Instruction templates optimized for reuse and context-awareness

🧠 Why use MCP?

• Build multi-step LLM workflows
• Maintain clean separation between tools, data, and logic
• Enable agents to "think" before taking actions
• Easily expose your backend as callable tools for an AI model

Example use case: building a customer support agent that reads from your database, files a ticket, and replies in natural language — all orchestrated via tools and context.