What I Learned Testing MCPs Across Claude, Cursor, and Replit
Source: Dev.to
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—particularly Claude, Cursor, and Replit—and I want to share what actually works, where things tend to break, and how I think about validation.
Not All MCPs Behave the Same Everywhere
- Small configuration differences matter.
- An MCP that runs in Claude can break in Cursor not because of a logic bug but due to subtle differences in how CLI tools, file paths, or quirky settings are handled.
Validation Is Hard
There isn’t a silver bullet for catching silent failures. Most of the time I:
- Run the same MCP in minimal contexts.
- Check raw outputs and side effects.
- Isolate tool chains until I understand exactly where it fails.
Trending MCPs You Might Find Useful
MCPs are being built for a variety of workflows, from accessing files or GitHub repositories to analytics and Redis access. Treat them as reusable modules—not one‑off scripts—and validate each one in each environment before using it in production.
Resources
A curated list of MCPs that run across multiple environments can be found here:
Call for Feedback
I’d love to hear how others validate MCPs in their workflows. Feel free to share your experiences and tips.