This study presents the development and evaluation of ChatMolData, a multimodal AI agent designed to bridge the gap between experimenters and computational tools in molecular sciences. ChatMolData effectively processes various types of molecular data, including molecular images, structure-specific files and unstructured & structured data format, thereby overcoming the limitations of single-modal LLM agent. Through natural language queries, users without programming skills can achieve molecular data extraction, structuring, prediction, visualization, and search with high efficiency and accuracy under varying conditions. Results of the evaluation by expert chemists indicated that versatility and efficiency of ChatMolData are due to the integration of strong reasoning and perceptive LLM, elaborate prompt engineering, and various specialized molecular processing tools. Accordingly, our findings highlight that ChatMolData can be a transformative agent in molecular research, facilitating the adaptation to big data, expansion of data size, understanding of the dataset and design of novel structure. As the field of multimodal AI continues to evolve, systems like ChatMolData offer a promising direction for the integration of advanced AI agents in scientific research, fostering collaboration between experimenters and algorithm developers and accelerating progress in molecular sciences.