arXiv Machine Learning
Tool Recommendation for Multi-Step Agents: Finding Every Tool a Task Needs
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arXiv AI
1d ago
Explore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool Libraries
arXiv:2610.22688v1 Announce Type: cross Abstract: Large language model agents must discover relevant tools from large libraries, e.g., thousands of tools, as new needs arise during task execution. Wh...
arXiv AI
Sep 15
MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use
arXiv:2512.24565v4 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol...
arXiv AI
Oct 12
Do More Agents Help? Evaluating Accuracy and Cost in LLM Multi-Agent Workflows
The paper introduces BenchAgent, a framework for evaluating multi‑agent large language model (LLM) workflows against a single‑agent baseline, BenchAgent Core. Using GPT‑4.1 across ten reasoning, coding, and tool‑use benchmarks, the study compares six multi‑agent configurations, finding that EvoAgent achieves the highest mean pooled accuracy (79.70%) while other agents lag by 1.71–8.76 percentage points. The analysis also highlights trade‑offs in token consumption and accuracy, offering guidance for selecting and optimizing LLM multi‑agent workflows.
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 Machine Learning
Sep 22
Toollery: Scaling LLM Agents to Thousands of Skills and Tools
Toollery is a training‑free framework that compresses candidate lists for large language model agents, enabling efficient selection from thousands of skills and tools. It generates user‑intent queries from each skill or tool specification, builds a retrieval index, and limits online selection to a compact top‑k set before the LLM makes its final decision. Evaluations on the SkillRouter benchmark, BFCL‑V4, and a proprietary smart‑cockpit dataset show that Toollery improves recall and end‑to‑end selection while keeping selection costs bounded.
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.