LLMs in the Loop: AI Agents for Precision Telescope Command

Operating ground-based telescopes requires coordinating multiple software systems for orbital prediction, scheduling, execution, and data retrieval, tasks that are often labor-intensive and prone to error. We present an agentic framework that uses Large Language Models (LLMs) to translate natural-language observation requests into structured telescope commands. The agent interacts with a Telescope Automation Pipeline through Model Context Protocol (MCP) tools that provide access to telescope metadata, satellite-pass prediction, observation submission, and astrometric reporting. A web interface exposes the agent’s reasoning steps and tool calls, enabling transparent, conversational control of the full observation workflow. We evaluate several contemporary LLMs across representative telescope-operations scenarios. Models such as GPT-4o, Claude Opus 4.5, and Kimi K2 reliably executed required tool sequences, while others struggled with correct argument generation, preventing task completion. Results show that tool-use reliability, rather than general language ability, is the primary determinant of successful autonomous operation. This framework offers a path toward intuitive, low-overhead telescope control and future extensions to real-time analysis and adaptive scheduling.