MCP vs API is the fight every AI developer runs into, and most people are comparing the wrong two things.
In this video we settle it properly. We show what MCP actually is, where it sits inside your application, and why it does not replace the APIs you already have. Then we take one real task, summarizing yesterday's messages from a Slack channel, and build it two ways: once by handing the agent raw API docs and a sandbox, and once with an MCP server. The difference between them is the part almost everyone misses.
? What you'll learn:
1️⃣ Who actually executes an API call in an AI app (and why it is never the model)
2️⃣ The scaling math that pushed teams toward MCP
3️⃣ How MCP compares to just handing your agent the OpenAPI spec
4️⃣ When a direct integration is the smarter call, and when MCP starts paying off
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⏰ Timestamps:
00:00 - The MCP vs API debate
00:35 - What an API actually is
01:31 - The situation before MCP
03:56 - How MCP changes the picture
05:08 - Inside an MCP server and discovery
05:51 - A real-world use case
06:58 - Does MCP replace APIs?
07:18 - Why do you need MCP?
07:47 - Approach 1: Solving the task with the API directly
10:48 - Approach 2: Solving the same task with an MCP server
11:58 - The real difference between MCP and API
13:00 - Why not just give the model the OpenAPI spec?
14:32 - Two honest warnings: context and API keys
15:37 - When to use which
16:36 - Conclusion
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