What Exactly is MCP (Model Context Protocol)?

A 2-minute explanation

Here is a 2-minute, easy-to-understand explanation of Model Context Protocol (MCP) and how it works with LLMs:

Imagine you are chatting with an AI assistant like ChatGPT. You ask:

“What’s the weather in Pune?”

Now, normally, the AI might just guess or hallucinate the answer. But with Model Context Protocol (MCP), the AI does something much smarter-it reaches out to a real tool (like a weather API) to fetch the actual data.

How does the LLM know what to do?

Before answering, the AI is given a list of tools it can use-almost like a menu of services. Each tool has a clear description, the inputs it needs, and the format of the response.

For example, it’s told:

There’s a tool called "get_weather" that takes a "city" and returns temperature and condition.

What Happens Next?

When you ask your question, the AI uses this information and generates a structured request like this:

{ "tool": "get_weather", "parameters": { "city": "Pune" }}

This message is sent to an MCP server, which runs the actual tool and returns real data like:

{ "temperature_celsius": 32, "condition": "Sunny"}

The AI sees this and responds to you with:“The current weather in Pune is 32°C and sunny.”

Why is MCP powerful?

Reliable: The tools have fixed input/output formats, so the AI can use them without guessing.

Safe: If the AI sends a wrong input, the server rejects it clearly.

Traceable: Every tool call and response is logged - you can replay it and verify what happened.

Flexible: You can plug in any tool - math engines, databases, internal APIs - and the AI can use them all through the same protocol.

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