Every AI model today faces the same problem: it's trapped inside a box. It can think, but it can't do anything in the real world -- no file access, no database queries, no API calls -- unless you wire up each integration by hand.
Model Context Protocol (MCP) fixes this. It's an open standard that gives AI models a universal way to connect to tools and data. Think of it as USB-C for AI: one protocol, any tool, any model.
In this tutorial, you'll build a working MCP server in Python from scratch.
What is MCP?
MCP is an open protocol created by Anthropic in November 2024, now governed by the Linux Foundation's Agentic AI Foundation with backing from OpenAI, Google, Microsoft, AWS, and Cloudflare.
The numbers speak for themselves:
- 97 million monthly SDK downloads
- 21,600+ GitHub stars on the Python SDK
- Supported by Claude, ChatGPT, VS Code, Cursor, JetBrains, and more
- $6.3M raised by Manufact (Feb 2026) just for MCP tooling
Architecture: Hosts, Clients, Servers
┌─────────────────────────────────────────┐
│ MCP Host (Claude Desktop, VS Code) │
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Client 1 │ │ Client 2 │ ... │
│ └────┬─────┘ └────┬─────┘ │
└───────┼──────────────┼──────────────────┘
│ │
┌────▼────┐ ┌────▼────┐
│ Server A│ │ Server B│
│(Database)│ │(GitHub) │
└─────────┘ └─────────┘
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