Accurate grading of rhinitis severity in nasal endoscopy relies heavily on the characterization of key secretion types, notably clear nasal discharge (CND) and purulent nasal secretion (PUS). However, both exhibit ambiguous appearance and high structural variability, posing challenges to automated grading under weak supervision. To address this, we propose Multimodal Learning for Mucus Anomaly Grading (MMAG), which integrates structured prompts with rank-aware vision-language modeling for joint detection and grading. Attribute prompts are constructed from clinical descriptors (e.g., secretion type, severity, location) and aligned with multi-level visual features via a dual-branch encoder. During inference, the model localizes mucus anomalies and maps the input image to severity-specific prompts (e.g., “moderate pus”), projecting them into a rank-aware feature space for progressive similarity scoring.Extensive evaluations on CND and PUS datasets show that our method achieves consistent gains over Baseline, improving AUC by 6.31% and 4.79%, and F1 score by 12.85% and 6.03%, respectively.This framework enables interpretable, annotation-efficient, and semantically grounded assessment of rhinitis severity based on mucus anomalies.