Advancements in Large Language Model (LLM) Role-Playing Agents have focused on various construction methodologies, yet it remains unclear which aspects of character profiles genuinely drive role-playing quality. To bridge this gap, we introduce a systematic diagnostic framework that disentangles the impact of character profiles along three axes: Familiarity (Known vs. Unknown), Structure (Structured vs. Unstructured), and Disposition (Moral vs. Immoral). To investigate these axes, we design a unified hierarchical schema (5 dimensions, 28 fields) standardizing character attributes and construct a controlled dataset of 211 personas varying along these three axes. We evaluate five LLMs on single and multi-turn benchmarks. Our results reveal a striking asymmetry: Familiarity and Structure show negligible impact, while Valence produces large, consistent performance degradation for immoral characters across all conditions. This performance drop concentrates in motivation-related attributes, indicating that alignment priors actively suppress tokens needed for faithful immoral portrayal. To mitigate this alignment-induced bottleneck, we propose Field-Aware Contrastive Decoding (FACD), a training-free strategy that selectively amplizes suppressed immoral-field signals, significantly reducing the Moral-Immoral performance gap without sacrificing moral-character performance.

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