MCP as a concept is amazing—however, 90% of its implementations are trash

Model Context Protocol (MCP) servers act as converters between general tools (ERP, analytics tools, or others) and AI communication systems. I have seen so many errors over the last few days that I wanted to offer a perspective without the hype. Furthermore, there are YouTube videos with hundreds of thousands of views promising that you can create an MCP server in less than 30 minutes. Not sure if this should be the standard you want with your task

To explain the process and resulting risks, here is a simplified explanation:

• MCP provides a set of instructions on how the AI can use the system.
• The user makes a request to the AI.
• The AI interprets the request based on the provided instructions and its inherent knowledge.
• The background code on the MCP server is executed and sends its information back to the AI.
• The AI uses the provided information to formulate an answer to the user.

There are four major risks in this process:

1. The instructions sent by the MCP server are not under your control.
2. Humans make mistakes—spelling errors or slight miscommunications may not be handled appropriately.
3. LLMs make mistakes. Anyone who has tried “vibe coding” will confirm that hallucinations in operational systems are unacceptable.
4. It remains unclear what MCP is actually doing. Given all these risks, is it wise to use a system whose capabilities are not fully understood?

In this constellation, it’s just a question of time until there will be a mistake. The primary question is how well the system is set up to avoid significant issues.

For now, I advise exercising caution with MCP and using it only in scenarios where the system is strictly read-only. For future implementations, I strongly recommend establishing clear guidelines for using MCP and adopting open-source solutions for transparency.

What are you experiences with MCP? Do you have any strategies to avoid problems / hallucinations?