TBNAI
Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions
Publication: cs.AI updates on arXiv.org
Author: Xiaofan Li, Fengchun Qiao, Xing Gao
Source published: 14/10/2026, 04:00:00 UTC
Briefing created: 25/10/2026, 18:58:39 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 14/10/2026, 12:19:27 UTC.
Briefings
A 2026 study analyzed 367,174 MCP tools to determine if parameter schemas influence LLM tool selection. Researchers found that schema changes cause the largest selection shifts, often favoring separate top-level parameters over grouped forms. This suggests developers can affect which implementation is called even when functionality remains unchanged.
Key points
- Parameter schema changes produce the largest and most consistent selection shifts among tested interventions.
- Models tend to favor separate top-level parameters over grouped or composite forms.
Context
This research investigates whether metadata structure affects LLM tool selection in Model Context Protocol (MCP) systems, specifically examining if schema design influences which implementation is invoked.
Limitations
Analysis covers 367,174 tools across five LLMs and five tasks using conditional logistic regression. Results are based on controlled factor-level interventions and may not generalize to all real-world scenarios.
Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 25/10/2026, 18:58:39 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.
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