我如何在一個週末構建 3 個 MCP 伺服器（以及我的心得）

I've been building B2B APIs for a while, but recently I got into MCP (Model Context Protocol). It's a game-changer for AI agents.

In this post, I'll share how I built 3 MCP servers in a weekend — and what I learned.

What is MCP?

MCP is a protocol that allows AI agents (like Claude, ChatGPT) to call external tools. Instead of giving an AI a huge prompt, you give it a tool it can call. It's faster, cheaper, and more reliable.

The 3 MCP Servers

1. Currency Exchange MCP

What it does: Gets real-time exchange rates for 160+ currencies.

Why I built it: Most AI agents don't have access to live currency data. This fixes that.

GitHub: currency-exchange-mcp

2. Sanctions MCP

What it does: Screens entities against global sanctions lists (OFAC, UK, EU, UN).

Why I built it: Compliance is a huge pain for fintech. This makes it easy for AI agents to do it.

GitHub: sanctions-mcp

3. HS Codes MCP

What it does: Classifies any product into a Harmonized System (HS) code.

Why I built it: Importers and exporters need this. Now AI agents can do it in seconds.

GitHub: hs-codes-mcp

What I Learned

1. MCP is simple. The SDK is well-designed. You can build a server in ~30 lines of code.
2. Glama.ai is the best directory. They accepted my servers in minutes.
3. AI agents are the future. More and more people will use MCP to connect tools.

Try Them Out

All 3 servers are open-source and available on Glama.ai.

You can also check out my other APIs at jsonexlab.com.

What are you building with MCP? Let me know in the comments!