Vision-Language Models (VLMs) achieve strong multimodal understanding by encoding images as sequences of visual tokens, yet cost hundreds and thousands of tokens per image. Existing pruning methods often rely on a single criterion, capturing only a partial view of token necessity. Controlled experiments reveal that token necessity is multi-faceted, requiring the joint exploitation of heterogeneous pruning perspectives. To address this, we propose **MVP-Token**, a novel Multi-View-Guided Token Pruning method to learn token necessity from multiple complementary pruning perspectives. We first establish a dataset **MVP-PruneSet**, which encompasses diverse pruning criteria by running orthogonal pruning methods, including a novel question-guided method **GRASP** at the encoder side. We then propose **MVP-Pruner** to capture such multi-view supervision signals. Specifically, a lightweight dual-attention module predicts soft token-importance scores under multi-view guidance. Extensive experiments demonstrate the effectiveness of MVP-Token across different model architectures. On LLaVA-1.5-7B, our method can surpass the single-view baseline by a clear margin. On Qwen2.5-VL-7B, our method achieves a 1.33× speedup and a 24.6% latency reduction under dynamic resolution and surpasses VisionZip by 1.87% of the average performance over two retention ratios.