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LLM tool selection heavily influenced by parameter schema, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates how Large Language Models (LLMs) select tools within Model Context Protocol (MCP) systems. The research found that the parameter schema, which defines how a tool accepts input, has a more significant impact on tool selection than tool names or descriptions. LLMs showed a preference for tools with separate top-level parameters over grouped or composite forms. The study also indicated that descriptions explaining parameters can increase selection, while the effect of tool names is less consistent. Ultimately, the parameter schema influences not only how tools are called but also whether they are selected, suggesting that schema design and tool ordering are crucial for MCP developers. AI

IMPACT This research highlights the importance of structured metadata for LLM tool selection, potentially guiding the development of more effective AI agents and systems.

RANK_REASON Research paper published on arXiv detailing LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM tool selection heavily influenced by parameter schema, study finds

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18 / 100
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Tool
Research paper published on arXiv detailing LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaofan Li, Fengchun Qiao, Xing Gao ·

    Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions

    arXiv:2610.14334v1 Announce Type: new Abstract: In Model Context Protocol (MCP) systems, LLMs select tools from metadata rather than from their implementations. Names and descriptions summarize what a tool does, while the parameter schema specifies the fields a tool accepts and h…

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