Explain This Error: AI-Powered API Diagnostics via MCP [SmartBear MCP Hackathon]

Our team recently explored a prototype called Explain This Error, built during the SmartBear hackathon. The idea came from one of the most universal developer frustrations: trying to understand why an API error happened, what caused it, and how to fix it. All while jumping between multiple tools.

The Problem

When something breaks, developers usually find themselves moving between BugSnag for logs, Swagger Portal for documentation, and GitHub for community fixes or past discussions. Each of these tools is powerful, but the context lives in different places.

That fragmentation means a simple debugging session often turns into a scavenger hunt — copying stack traces, searching for endpoint details, checking changelogs, and digging through GitHub issues. It’s time-consuming, interrupts flow, and often leaves developers guessing instead of learning.

The Idea

Explain This Error aims to change that. Using the SmartBear MCP Server, our prototype connects BugSnag, Swagger Portal, and GitHub to automatically diagnose API errors and explain them in human language, directly within your IDE or the Portal itself.

Here’s how it works in practice:

When a new API error appears in BugSnag, MCP gathers all related data, the stack trace, endpoint information, and metadata. It then fetches relevant documentation and changelog entries from the Swagger Portal and, if necessary, searches GitHub discussions for community-provided answers.

The AI analyzes everything together, identifies the likely cause, and provides a contextual explanation. What happened, why it happened, and how to fix it. It even links to relevant docs or issues. If a suggestion comes from a forum, it’s clearly marked as “Answer from community” so developers know the source.

The Prototype

This project is currently a prototype, developed as part of the hackathon to demonstrate how MCP could power contextual, AI-driven diagnostics.

While it’s still early, the results were promising. Seeing an API error in the IDE and immediately getting a concise explanation — along with documentation and potential fixes — made the debugging experience smoother and more intuitive.

The next steps would be exploring tighter integration into SmartBear’s developer tools ecosystem and adding deeper IDE hooks for real-time context awareness.

Why It Matters

Debugging should be a learning experience, not a guessing game. By turning scattered data into guided insight, Explain This Error helps developers fix problems faster and understand their systems better.

It directly impacts key developer experience metrics like time to resolution and satisfaction, while also driving more engagement with the Swagger Portal and other SmartBear documentation resources. For teams adopting MCP, this kind of contextual AI layer shows what’s possible when we blend observability, documentation, and intelligence into one cohesive workflow.

What’s Next

For now, Explain This Error remains a hackathon prototype, but the potential is clear. We’d love to hear from the community! How do you currently troubleshoot API errors? What kind of context or guidance would make your debugging faster and less frustrating?