Vision-language models (VLMs) exhibit promising generalization capabilities, yet face considerable challenges when adapting to domain shifts stemming from changes in data distributions. Test-time adaptation (TTA) has thus emerged as a promising approach for enhancing VLM performance under such conditions. In practice, test data often arrives in batches, which has led to increasing interest in the transductive TTA setting. Existing TTA methods, however, are typically limited by focusing solely on individual test samples, thereby overlooking the critical cross-sample correlations within a batch. While recent ViT-based TTA methods have started to incorporate batch-level adaptation, they remain suboptimal for VLMs due to insufficient integration of the essential text modality. To bridge key gaps in TTA for VLMs, we propose a novel transductive TTA framework called Supportive Clique-based Attribute Prompting (SCAP), which effectively combines visual and textual information to enhance adaptation by generating fine-grained attribute prompts across test batches. SCAP first unsupervisedly forms supportive cliques of test samples based on visual similarity and learns an attribute prompt for each clique, capturing shared attributes critical for adaptation. For each test sample, SCAP aggregates attribute prompts from its associated cliques, providing enriched contextual information. To ensure adaptability over time, we incorporate a retention module that dynamically updates attribute prompts and their associated attributes as new data arrives. Comprehensive experiments across multiple benchmarks demonstrate that SCAP outperforms existing state-of-the-art methods, significantly advancing VLM generalization under domain shifts. The code will be released.