Tool and Agent Selection for Large Language Model Agents in Production: A Survey

Large Language Model (LLM) agents have demonstrated remarkable abilities to interact with external tools, functions, Model Context Protocol (MCP) servers, and agents. Due to the fast-paced nature of the industry, existing literature does not accurately represent the current state of tool and agent selection. This work provides the first detailed examination of tool selection from a production perspective, distinguishing between the frontend layer where users interact with agents through buttons, slash commands, or natural language, and the backend layer where retrieval, execution, orchestration, and memory enable scalable reasoning. The paper contributes a unified taxonomy of modern tool and agent selection approaches, a production-oriented framework covering dynamic tool retrieval, context engineering, execution methods, memory, and evaluation, and identifies challenges and promising avenues for research.