AI 中文总结
研究如何通过模型上下文协议将本地大语言模型与科学仪器相连，结合多种技术实现连接，开源服务器展示相关工具等，通过软件验证，为闭环智能仪器研究提供基础。
AI 中文摘要
大语言模型（LLMs）可规划工具介导的科学工作，但连接科学仪器仍困难重重。我们提出一种通过模型上下文协议（MCP）将本地LLMs与科学仪器相连的方法。它结合了模式绑定工具表面、供应商中立的主机进程适配器模式、长期运行实时处理作业的持久生命周期以及MCP提示注册技能。开源参考服务器通过物理合理模拟器展示了多种工具、作业类型和技能。验证仅通过软件进行，该方法为闭环智能仪器研究提供了有界、可测试和可重现的基础。
英文摘要
Large language models (LLMs) can plan tool-mediated scientific work, but scientific instruments remain difficult to connect to such agents: vendor APIs may load only inside acquisition host processes, facilities may prohibit cloud-hosted agents, and natural-language interfaces can emit physically unreasonable arguments. We present a method for connecting local LLMs to scientific instruments through the Model Context Protocol (MCP). It combines: (1) a schema-bound tool surface that validates requests against physical bounds before adapter dispatch; (2) a vendor-neutral, host-process adapter pattern separating language-side reasoning from instrument-side execution; (3) a persistent lifecycle for long-running live-processing jobs; and (4) MCP-prompt-registered skills that compose typed tools into reusable multi-step protocols. Our open-source reference server exposes 30 typed tools, 5 live-job types, and 6 skills through a physics-plausible simulator implementing the same protocol surface. Validation is software-only: all 120 hardware-independent tests pass deterministically, while 15 local-LLM integration tests pass 12-15 of 15 across runs because of model nondeterminism. A preliminary single-run probe across five open tool-calling LLMs indicates that the schema-bound interface can be driven locally by small open-weight models without cloud dependency; it is not a benchmark and has no confidence intervals. The method provides a bounded, testable, and reproducible foundation for closed-loop agentic instrumentation research.
Comments36 pages, 8 figures, 7 tables. Open-source reference implementation, physics-plausible simulator, and validation suite. Software-only validation; live-instrument validation is not reported
