ContextOptimizer

I built ContextOptimizer after spending countless frustrating hours debugging conversations between AI agents in my multi-agent systems. The breaking point came when a complex workflow kept failing because agents were misinterpreting each other's outputs, and I realized there were no specialized tools to solve this problem. What makes this unique is the systematic approach to what's typically an ad-hoc process. Instead of blindly tweaking prompts through trial and error, ContextOptimizer provides data-driven analysis across 5 dimensions (prompt clarity, context flow, tool integration, error handling, and performance) to pinpoint exactly where communication breaks down. I'm most proud of the coordinated optimization feature - it simultaneously improves both system prompts and tool return formats, which is crucial since they need to work together. The tool transforms context engineering from an art into a repeatable engineering process, complete with quantifiable metrics and actionable recommendations. For anyone building multi-agent systems, this eliminates hours of debugging and makes your AI workflows dramatically more reliable. Would love to hear if others are facing similar challenges!