What is MCP? (Explained for non-developers)
Introduction Hey everyone! Today we're breaking down Model Context Protocol (MCP) in the simplest way possible. I want to show you how MCP makes AI agents more intelligent and why this matters for anyone working with AI automation. The Basics: How AI Works Today Let's start with the fundamentals. Think about ChatGPT - it's pretty straightforward: - Input: You ask a question ("Help me write this email" or "Tell me a joke") - Processing: The LLM thinks about your request - Output: You get an answer This works great for basic conversations, but it's limited. The Evolution: AI Agents with Tools The next big leap was giving LLMs tools - that's when we got AI agents. But here's where things get interesting, and also where we hit some limitations. How Current Tools Work (And Their Limitations) Each tool has a very specific function, and here's the problem - they're not super flexible. Why? Because within each tool configuration, we basically have to hardcode: - The operation (what am I doing?) - The resource (what am I working with?) - Dynamic parameters (like message IDs or label IDs) For example: - Operation: "Get" | Resource: "Message" (this never changes) - Operation: "Send" | Resource: "Message" (this never changes) You can see how rigid this becomes. Enter MCP Servers: The Game Changer Now, here's where MCP servers come in and completely transform the game. What is an MCP Server? Think of an MCP server as a universal translator that sits between your AI agent and the tools you want to use. How It Works When your agent sends a request to an MCP server (let's say Notion), it gets back much more than just "here are your available tools." The MCP server provides: - Available tools and their functionality - Resource information (what can I access?) - Schemas (how should I format requests?) - Prompts (how should I interact?) The MCP server takes all this information and helps the agent understand exactly how to take the action you requested in your original input.