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MCP Evaluation Methodologies
Evaluating MCP systems without proper methodologies is like trying to judge a cooking competition without tasting a dish.
I've spent a lot of my career trying to figure out whether AI systems work. Not just whether they run without crashing, which is the easy part, but whether they're solving the problems they're supposed to solve, whether they're reliable enough for production use, and whether they're worth the investment in time and resources.
Evaluation of traditional AI systems is already challenging. You've got accuracy metrics, performance benchmarks, user satisfaction surveys, and a dozen other ways to measure success. But with MCP systems, the evaluation challenge becomes exponentially more complex because ...
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