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
Why it has this license class
AChecked 30 Sept 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.
| Source | License | Open-access status | Read as |
|---|---|---|---|
| openalex | cc-by | gold | Green |
| crossref | https://creativecommons.org/licenses/by/4.0/ | — | Green |
| unpaywall | cc-by | gold | Green |
| europepmc | cc by | — | Green |
Abstract
BLarge 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: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an ecosystem of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic “AI scientist”. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.
Claims built on this paper
D- MCP is emerging as shared infrastructure across the production-agentic literature: Eranga et al. 2025 prescribe tool-first design over MCP with clean separation between workflow logic and MCP servers, while Yue et al. 2026's perspective on MCP-native AI scientist ecosystems cites MCP construction tooling such as Code2MCP alongside work on measuring agents in production. Trace →