hypertool-mcp

# 🔬 Research & Performance Metrics ## Why Focused Toolsets Work The science behind HyperTool's approach is backed by extensive research on LLM tool usage and cognitive load. ## Key Research Papers ### 📊 Less is More: Optimizing Function Calling for LLM Execution (2024) **Key Findings:** - **89% accuracy** with <10 tools vs **32% accuracy** with 50+ tools - **71% improvement** in task completion rates with focused tool selections - LLMs struggle with "cognitive overload" when presented with too many options **Paper Link**: [arxiv.org/abs/2411.15399](https://arxiv.org/abs/2411.15399) **What This Means for You**: By limiting your AI to 5-10 tools per context, you're operating in the optimal performance zone identified by researchers. ### 🧠 Tool Learning with Large Language Models: A Survey (2024) **Key Findings:** - Context window constraints make large tool sets impractical - Tool selection accuracy degrades exponentially with tool count - Hierarchical tool organization (like toolsets) improves selection accuracy **Paper Link**: [arxiv.org/abs/2405.17935](https://arxiv.org/abs/2405.17935) **What This Means for You**: HyperTool's toolset approach aligns with best practices for tool organization in LLM systems. 1. "Less is More: Optimizing Function Calling for LLM Execution" (2024) 2. "Tool Learning with Large Language Models: A Survey" (2024) 3. "Cognitive Load in Human-AI Interaction" (2023) 4. "The Magical Number Seven, Plus or Minus Two" - George A. Miller (1956)