Parkinson's disease (PD) is a devastating movement disorder accelerating in prevalence globally, but the lack of precision symptom measurement has made it difficult to develop effective new therapies. The Unified Parkinson’s Disease Rating Scale (UPDRS) is the gold-standard label for assessing motor symptom severity, yet its scoring criteria are vague and subjective, resulting in coarse and noisy clinical assessments. While machine learning approaches could help to modernize PD symptom assessments by making them more quantitative, objective, and scalable, model development is hindered by the absence of publicly available video datasets for PD motor exams. Here, we introduce the TULIP dataset to bridge this gap. TULIP emphasizes precision and comprehensiveness, comprising multi-view video recordings (6 cameras) of all 25 UPDRS motor exam components, together with ratings by 3 clinical experts, in a cohort of Parkinson's patients and healthy controls. The multi-view recordings enable 3D reconstructions of body movement that better capture disease signatures than more conventional 2D methods. Using the dataset, we establish a baseline model for predicting UPDRS scores from 3D pose sequences, illustrating how existing diagnostics could be automated. Going forward, TULIP can be used to develop new precision diagnostics that transcend UPDRS scores to provide a deeper understanding of PD and its potential treatments.