In robotic perception, cross-granularity object detection is essential for identifying and localizing targets at varying levels of detail. Traditional detection methods often struggle to bridge the gap between coarse object detection and fine-grained component localization, limiting their ability to associate parts, such as a cup and its handle. Vision-language models (VLMs), while effective in spatial reasoning, face challenges in fine-grained detection due to the scarcity of annotated datasets. To address these issues, we first propose the chain-of-detection (CoD) framework, which focuses on guiding detection in a step-by-step manner from coarse recognition to fine-grained localization. During this process, we observe that existing detectors still lack sufficient capability in recognizing fine-grained components. To overcome this limitation, we further combine the CoD framework with Monte Carlo tree search (MCTS) to automatically generate fine-grained datasets, eliminating the need for manual labeling and significantly improving detector performance. Experiments show that our approach achieves an average improvement of 17.31% in robotic manipulation success rates for common objects, 51.39% for larger object operations, and about 50% in simulated environments. These results demonstrate the effectiveness of CoD in advancing cross-granularity detection and enhancing precise robotic manipulation. The implementation is publicly available at https://github.com/tinnel123666888/CoD and the CoD dataset is released at https://huggingface.co/datasets/tinnel123/CoD_dataset.