Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery.

Ling Yue, Ching-Yun Ko, Pin-Yu Chen, Shimin Di, Shaowu Pan

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations:

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