Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the capacity to parse and manipulate two- and three-dimensional relations, remains unclear. While humans easily master textbook-style spatial problems, current MLLMs exhibit substantially higher error rates, exposing a fundamental weakness in spatial comprehension. To investigate this gap, we present MathSpatial, the first large-scale and systematic dataset resource dedicated to mathematical spatial reasoning in MLLMs. MathSpatial provides two complementary subsets: MathSpatial-Bench (2,000 problems) for evaluating spatial reasoning isolated from perceptual noise, and MathSpatial-Corpus (8,000 problems) equipped with structured reasoning traces for training. Benchmarking 20+ MLLMs reveals a severe deficit: non-reasoning models largely languish below 60% accuracy, and even the strongest reasoning models fall ~16 percentage points behind human performance (96.3%). Crucially, we demonstrate that fine-tuning on MathSpatial-Corpus yields consistent performance gains across model families. MathSpatial provides a vital resource for diagnosing and advancing spatial reasoning in MLLMs. The dataset and related resources are available at https://github.com/shuolucs/MathSpatial.