Dynamic Function Registration — Runtime Tool Discovery

I will build a dynamic function registration system for your LLM application — enabling tools to be registered, updated, and removed at runtime without application restart, with a tool registry that supplies the appropriate function subset for each query through relevance-based tool selection rather than including all tools in every request. Static function registrations that include all tools in every request degrade in two ways as tool count grows — token cost increases linearly with every tool added, and function selection accuracy decreases as the model must distinguish between an ever-larger tool set; dynamic tool selection supplies only the relevant tools for each specific query, maintaining both token efficiency and selection accuracy at scale.

The system covers a tool registry with runtime registration and deregistration, embedding-based tool relevance scoring for each query, dynamic tool subset selection, registry API for administrative tool management, and token usage comparison between static and dynamic approaches.