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

TL;DR

The paper introduces NIMO Controller, an MCP-based orchestrator for self-driving laboratories that enables both human and AI interaction through a unified, standardized interface, demonstrated via a color-matching case study.

Contribution

It presents a novel MCP-based SDL architecture and a visual programming interface that simplifies workflow design for humans and AI agents.

Findings

Validated usability through a color-matching SDL case study.

Unified interface supports both human and AI interactions.

Demonstrated effective orchestration of SDL components.

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.