Time series anomaly detection (TSAD) is a critical task in scientific domains like astronomy, where identifying rare and scientifically meaningful events is important. However, existing TSAD methods face limitations when applied to astronomical settings. Generic methods often exhibit high false positive rates due to their neglect of domain-specific semantics. Moreover, the lack of interpretability undermines scientific trust, and single-modality approaches fail to leverage the diverse sources of knowledge. To address these challenges, we propose MAVI, a Multimodal large language model-enhanced Anomaly Validator and Interpreter tailored for astronomical TSAD. Rather than serving as a primary detector, MAVI operates as a lightweight post-processing framework that filters and explains anomaly candidates produced by base TSAD methods. MAVI introduces two novel innovations: first, a multimodal in-context learning strategy that retrieves numerical, visual, and textual anomaly templates to integrate multi-source knowledge; second, a domain-guided chain-of-thought prompting mechanism that emulates astronomers’ analytical reasoning to enhance both accuracy and interpretability. Experiments on six astronomical datasets show that MAVI substantially reduces false positives while maintaining high recall, and provides expert-aligned, interpretable rationales for anomaly decisions.