MCP vs API: Pick the Right Way to Connect AI
APIs have been around for years, but MCP is the new kid on the block. This series breaks down what each one does, how they differ, and when to use which. By the end, you'll know exactly how to connect your AI tools to the outside world.
What you'll learn
- Understand why AI models can't do everything on their own
- See real examples of AI needing outside tools like calendars and databases
- Learn what 'connecting AI to the outside world' actually means in plain terms
- Define what an API is using a simple restaurant analogy
- Understand how a request and response cycle works
- See why connecting lots of APIs requires lots of custom code
Chapters
- 1
Why Does AI Need to Talk to Other Tools?
Before comparing anything, you need to understand the basic problem. Your AI is smart, but it lives in a box. This chapter explains why connecting AI to outside tools and data matters so much.
- 2
How APIs Work (The Classic Approach)
APIs are how software has been talking to other software for decades. This chapter explains what an API is, how it works, and why developers have relied on them for so long. You'll also see where they start to get painful.
- 3
What Is MCP and How Is It Different?
MCP stands for Model Context Protocol. Think of it as a universal adapter that lets any AI talk to any tool using one shared language. This chapter explains what MCP is, how it works, and why people are excited about it.
- 4
How AI Finds and Uses Tools on Its Own
This is where MCP gets really interesting. Instead of a developer wiring up each tool by hand, the AI can discover what tools are available and figure out how to use them. This chapter walks through how automatic tool discovery works step by step.
- 5
When to Use APIs, MCP, or Both
Now you know how both approaches work. This final chapter gives you a simple decision framework. You'll learn when APIs are the better choice, when MCP shines, and how many real systems use both together.