Most MCP developers are shipping blind, hoping their tools improve agent performance without any rigorous evidence. An MCP server that returns slow or poorly shaped responses can cause agents to loop, hallucinate, or ignore the tool entirely. I ran into this firsthand when my company spent three weeks trying to evaluate our own MCP server using existing coding benchmarks, only to find that every available tool was designed to measure the LLM itself, not the tools it uses.
Benchmarking lets you quantify the actual impact of your server on agent success rates and token efficiency rather than relying on gut feeling. That gap is exactly why I built mcpbr: to give MCP developers a way to measure real performance differences with and without their tool in the loop.