arXiv AI
Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions
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arXiv AI
Aug 26
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway presents SCOUT, a system that addresses two major challenges in large language model (LLM) agent tool usage: a context‑engineering bottleneck and a tool discoverability barrier. SCOUT reframes tool exposure as a context‑selection problem, injecting only relevant tools into the model’s context window and providing two MCP meta‑tools—tool_search and execute_tool—to perform hybrid retrieval via BM25 and dense vector search. In production at PayPal, SCOUT cuts MCP tool‑token consumption by 99%, dramatically reducing per‑query inference cost while remaining model‑agnostic and requiring no client‑side changes.
arXiv AI
Aug 11
LLM within MCP Matters: Measuring Inefficient Resource Utilization Driven by LLMs
arXiv:2608. 08467v1 Announce Type: new Abstract: The Model Context Protocol (MCP) standardizes how servers expose data and tools to Large Language Models (LLMs).
Hugging Face Trending Papers
Aug 5
Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools
Agent evaluations tell us that a model picked the wrong tool, but rarely why. We introduce canary tools: diagnostic probe tools planted in an agent's Model Context Protocol (MCP) tool set, each engineered to probe one specific tool-selection weakness.
arXiv AI
Aug 6
Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools
arXiv:2608. 04719v1 Announce Type: new Abstract: Agent evaluations tell us that a model picked the wrong tool, but rarely why.
arXiv AI
Aug 25
From SQL Generation to Tool Selection: A Domain-Oriented Pattern for MCP Servers
arXiv:2608.22063v1 Announce Type: new Abstract: Agents built on Large Language Models (LLMs) increasingly reach enterprise data through the Model Context Protocol (MCP), and many MCP database servers...
arXiv AI
Jun 16
ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents
arXiv:2606. 15508v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly operate over large tool libraries, but existing evaluations often focus on whether a model can call a tool correctly rather than how the visible tool menu shapes reliability, efficiency, and safety-relevant risk exposure.