This paper introduces Intentional-Gesture, a novel framework for understanding and leveraging human communication intent to generate meaningful gestures. To address the reliance of existing methods on shallow linguistic cues, we treat gesture generation as an intent inference task based on high-level communication features. By building the InG dataset, adding gesture-intention annotations, and developing an Intentional Gesture Motion Tokenizer to enable intent-aware gesture synthesis, we achieve unprecedented performance on the BEAT-2 benchmark.