NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol
Self-driving laboratories (SDLs) accelerate scientific discovery but require technically demanding software. Existing orchestration frameworks focus on human interaction and lack standardized interfaces for AI agents. This work proposes an SDL software architecture based on the Model Context Protocol (MCP), exposing all SDL functionalities via MCP servers. The NIMO Controller provides a visual programming interface automatically generated through MCP tool discovery, allowing human users to design workflows without coding, while AI agents access the same backend. A color-matching SDL case study validates the architecture's usability.
[2605.15227] NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol
[Submitted on 13 May 2026]
Title:NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol
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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, 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
arXiv-issued DOI via DataCite
Submission history
From: Naruki Yoshikawa [view email] [v1] Wed, 13 May 2026 14:25:45 UTC (2,809 KB)
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