MCP is a great fit for some situations and overkill for others. Knowing the difference saves you effort.
Reach for MCP when you want an AI to reliably connect to real tools and data, especially across more than one tool or when you might switch AI models later. Because it's vendor-neutral, you can swap the model underneath without rewriting your integrations — the investment in connecting tools is protected.
You might not need MCP when you have a single, simple, one-off task with no reuse — a quick script might be faster. And remember from Module 1: MCP won't orchestrate a complex multi-step workflow for you; that needs additional tooling on top.
A Concrete Example
If you're building an assistant that needs to read your database, post to Slack, and manage GitHub issues — and you want to keep your options open on which AI model powers it — MCP is exactly right. If you just need a tiny script to rename some files once, wiring up an MCP server is more work than it's worth.
Use MCP when reuse, multiple tools, or model portability matter — skip it for trivial one-offs.