Early detection of frailty helps identify those at risk of adverse health outcomes, enabling interventions to reverse decline. Frailty is associated with changes in heart rate variability (HRV), and machine-learning-based assessment of HRV from the 6-minute walk test (6MWT) has proven effective for frailty assessment. Tabular Foundation Models (TFN), such as TabPFN, use pre-trained models to achieve accurate classification on small datasets while reducing the risk of overfitting. Therefore, the main goal of this paper is to assess whether TFN can improve frailty detection using HRV compared to traditional ML models. In this study, we used the Physionet database titled "Wearable-based signals during physical exercises from patients with frailty after open-heart surgery." Single-lead ECGs were obtained from patients during the 6MWT. Frailty was evaluated using the Edmonton Frail Scale, with a score of 5 or less indicating non-frailty and scores above 5 indicating frailty. After QRS detection, 36 HRV features were extracted. These features include statistical measures, frequency-based metrics, traditional Poincare features, geometric Poincare attributes, and heart rate asymmetry indices. Several machine learning classifiers were employed for frailty assessment, including TabPFN, random forest, XGBoost, and CatBoost. To ensure robust evaluation and facilitate an objective comparison of model performance, a 10-fold cross-validation strategy was implemented. ECG data from 67 individuals (29 non-frail, 38 frail) were analyzed. TabPFN, random forest, XGBoost, and CatBoost achieved AUROC scores of 0.60, 0.65, 0.62, and 0.65, respectively. Based on these findings, TFNs did not demonstrate superior performance over traditional machine learning methods for frailty assessment in settings with limited data. Notably, prior analysis using automated machine learning (autoML) on the same dataset achieved a higher AUROC than the classifiers employed in this paper. These results indicate that autoML may offer a more effective solution for frailty detection when training data is limited.