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Evaluation of modern large language model (LLM) systems increasingly relies on multi-stage pipelines such as retrieval-augmented generation, tool-using agents, and prompt chains. Reproducing reported evaluation results for these systems often requires evaluation-specific artifacts beyond model weights and datasets, including prompts, judge configurations, retrieval snapshots, and intermediate traces, yet their availability has not been systematically examined.We introduce ReproEvalCard, a lightweight reporting standard that specifies the minimal artifacts required to reproduce and validate evaluations of LLM pipelines. To motivate this standard, we audit 55 pipeline-based LLM papers published between 2022 and 2025 and quantify the availability of reproducibility-critical evaluation artifacts. We find that randomness controls are missing in 75% of papers and intermediate execution traces in 61%, substantially limiting evaluation reproducibility. We further demonstrate ReproEvalCard through a worked example and provide a concise checklist for authors and reviewers, aiming to improve reproducibility and comparability in LLM evaluation.