In the tabular domain, which is the predominant data format in real-world applications, anomalies are extremely rare or difficult to collect, as their identification often requires domain expertise. Consequently, evaluating tabular anomaly detection models is challenging, since anomalies may be absent even in evaluation sets. To tackle this challenge, prior works have generated synthetic anomaly generation rely on statistical patterns, they often overlook domain semantics and struggle to reflect the complex, domain-specific nature of real-world anomalies. We propose AutoAnoEval, a novel evaluation framework for tabular AD that constructs pseudo-evaluation sets with semantically grounded synthetic anomalies. Our approach leverages an iterative interaction between a Large Language Model (LLM) and a decision tree (DT): the LLM generates realistic anomaly conditions based on contextual semantics, while the DT provides structural guidance by capturing feature interactions inherent in the tabular data. This iterative loop ensures the generation of diverse anomaly conditions, ranging from easily detectable outliers to subtle cases near the decision boundary. Extensive experiments on 20 tabular AD benchmarks demonstrate that AutoAnoEval achieves superior model selection performance, with high ranking alignment and minimal performance gaps compared to evaluations on anomalies encountered in practical applications.