Member-only story
What is MCP and Why its important? | SDETs learn MCP please!
AI technology is growing really fast. While everyone’s buzzing about the latest models like GPT‑5, Gemini 2, or Claude 4, the real power of AI today isn’t just in having the newest models. It’s about how we connect these models to the world around them — giving them the right tools, data, and context to understand and act on real-time information.
That’s where Anthropic’s Model Context Protocol (MCP) comes in. MCP isn’t just an upgrade; it’s a game‑changer that will revolutionize the way we build smart AI agents. In this blog, we’ll break down MCP in a way that’s easy to understand, provide detailed examples and high‑level designs (HLDs), and point you to useful documentation and references. Trust me — this is not clickbait. MCP is the key to the future of AI interactions.
What is MCP?
Imagine you’re chatting with your AI assistant in Claude Desktop. You ask a question, and the AI needs more information to give you a complete answer. Instead of relying on static training data (which might be outdated), it uses MCP to reach out to specialized tools (called MCP Servers) that can fetch live data. Think of MCP as a smart translator between you and your AI assistant.
Quick Analogy:
Your Question: “What’s the weather in New York?”
Without MCP: The AI might reply with old data from its training.
With MCP:
- Your question is sent from the MCP Host (Claude Desktop) via an MCP Client.
- The client contacts an external MCP Server that handles weather data.
- The server fetches current weather information and sends it back.
- The AI combines this live data with its answer: “Good morning! It’s sunny and 75°F in New York.”
Pointers:
- For the official protocol specification and SDKs, visit modelcontextprotocol.io and check out their GitHub repositories.
Why MCP is a Game-Changer
1. Connecting AI to Live Data
Large Language Models (LLMs) like ChatGPT or Claude are incredibly smart — but they have a major limitation: they…
