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NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol

arXiv AI Archived May 18, 2026 ✓ Full text saved

arXiv:2605.15227v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) have attracted increasing attention as a means of accelerating scientific discovery; however, developing SDL software remains technically demanding. To improve accessibility, orchestration software frameworks have been proposed to coordinate SDL components. Nevertheless, existing frameworks are primarily designed for human interaction and do not provide standardized interfaces suitable for AI agents. In this work, w

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    Computer Science > Artificial Intelligence [Submitted on 13 May 2026] NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol Naruki Yoshikawa, Ryo Tamura Self-driving laboratories (SDLs) have attracted increasing attention as a means of accelerating scientific discovery; however, developing SDL software remains technically demanding. To improve accessibility, orchestration software frameworks have been proposed to coordinate SDL components. Nevertheless, existing frameworks are primarily designed for human interaction and do not provide standardized interfaces suitable for AI agents. In this work, we propose an SDL software architecture based on the Model Context Protocol (MCP), in which all SDL functionalities are exposed through MCP servers. Following this design principle, we introduce an MCP-based SDL orchestrator, named NIMO Controller. It provides a visual programming interface automatically generated through MCP-based tool discovery, allowing human users to design experimental workflows without writing code. The same MCP backend can also be accessed by AI agents, providing a unified interface for both human users and AI agents. We demonstrate the proposed system through a case study on a color-matching SDL. The results validate the usability of the proposed MCP-based SDL architecture. Comments: 9 pages, 4 figures Subjects: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Robotics (cs.RO) Cite as: arXiv:2605.15227 [cs.AI]   (or arXiv:2605.15227v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.15227 Focus to learn more Submission history From: Naruki Yoshikawa [view email] [v1] Wed, 13 May 2026 14:25:45 UTC (2,809 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cond-mat cond-mat.mtrl-sci cs cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    May 18, 2026
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    May 18, 2026
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