When MCP (Model Context Protocol) first appeared, there was a lot of buzz. It was described as the key to giving AI a “plugin system” — models would no longer just chat, but also call external tools, access data, and perform tasks. In theory, it sounded like giving AI real “hands and feet.”
I was quite excited at the beginning and even thought it might become the silver bullet for AI applications.
But today, the picture looks different
- Hype has cooled: There was plenty of noise on Twitter and GitHub when it first launched, but things have clearly quieted down. MCP is still being updated, but it hasn’t grown into a mainstream developer ecosystem.
- Practicality in doubt: The protocol is neat, but in real projects it doesn’t feel easy to adopt. Integration comes with friction, and most examples remain at the demo stage.
- Security concerns: Especially with the stdio approach, where processes talk directly to each other. The boundaries feel blurry, and giving AI that level of access always raises concerns.
- Not widely adopted: If MCP were truly a silver bullet, we would already see broader adoption among indie developers. But so far, it hasn’t reached production-level use in most cases.
Not a silver bullet — just an experiment
To be fair, MCP is still a valuable exploration:
- It pushes AI one step closer to “taking action,” not just talking.
- It shows the possibility of a unified interface across different models.
But it’s not a silver bullet, at least not yet. The standard isn’t stable, there’s no killer application, and the security story remains unsettled.
My view
At this point, MCP feels more like an experimental protocol than a technology ready for mainstream adoption. As an indie developer, I’ll keep an eye on it, but I wouldn’t bet too much on it yet.
In short: MCP is not the thing that will make AI take off — it’s just one interesting step along the way.