Developer finds LLM eval was mislabeling correct model behavior as failure

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A developer building an LLM-powered evaluation tool for MCP servers discovered that a seemingly perfect server, context7, was incorrectly flagged as failing tool selection 62% of the time. The root cause was that the benchmark's task synthesizer generated prompts requiring multi-step tool pipelines — such as fetching a thread ID before replying — but graded the model wrong when it reasoned accordingly. After adding a constraint requiring all task prompts to embed concrete parameter values, context7's score jumped from 38% to 100% and Slack's from 54% to 100%. Further testing on a 26-tool server, Firecrawl, validated the fixed eval: selection failures clustered around naming collisions already flagged by static analysis, confirming that cheap static linting can predict live model confusion. The case highlights a widespread flaw in agent benchmarks, where multi-step tool dependencies quietly inflate failure rates and misrepresent model capability.

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