Model Context Protocols (MCPs) are a powerful pattern for connecting AI assistants to real world data sources and tools. Over the past few months, I’ve been experimenting with MCPs across multiple environments in particular Claude, Cursor, and Replit and I want to share what actually works, where things tend to break, and how I think about validation.
MCPs are not magic they are just a standard for letting LLM based tools talk to external systems in a reliable way. This is similar to how a USB-C port lets different devices connect to your computer: the protocol itself doesn’t do anything special, but it makes integration possible when done right.
What I observed
1. Not all MCPs behave the same everywhere
A lot of MCP examples that...
Model Context Protocols (MCPs) are a powerful pattern for connecting AI assistants to real world data sources and tools. Over the past few months, I’ve been experimenting with MCPs across multiple environments in particular Claude, Cursor, and Replit and I want to share what actually works, where things tend to break, and how I think about validation.
MCPs are not magic they are just a standard for letting LLM based tools talk to external systems in a reliable way. This is similar to how a USB-C port lets different devices connect to your computer: the protocol itself doesn’t do anything special, but it makes integration possible when done right.
What I observed
1. Not all MCPs behave the same everywhere
A lot of MCP examples that feel solid in one environment print nothing or fail silently in another. This usually comes down to how each environment handles execution, context, filesystem access, or tool arguments.
2. Small config differences matter
Sometimes an MCP that runs in Claude breaks in Cursor not because of a logic bug but because of subtle differences in how CLI tools, paths, or quirk settings are handled.
3. Validation is hard
There isn’t a silver bullet yet for catching silent failures. Most of the time, I find myself running the same MCP in minimal contexts, checking raw outputs and side effects, and isolating tool chains until I understand exactly where it fails.
4. Trending MCPs you might find useful
From systems that access files or GitHub repos to tooling that helps with analytics or Redis access, MCPs are being built for a variety of workflows. Treat them as reusable modules, not one off scripts.
What this means for you
If you’re building with MCPs, treat them as execution boundaries with behavioral contracts you should validate each one in each environment before using it in production workflows.
For folks discovering MCPs or trying to find working examples across tools like Claude, Cursor, GitHub Copilot and more, I’ve been aggregating what actually runs in multiple environments.
Here’s a place where that’s organized for easy reference:
https://ai-stack.dev/
Would love to hear how others validate MCPs in their workflows.