Tool Discovery for LLM Agents

LLM: Large Language Model

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Latest papers

16 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

Tool Recommendation for Multi-Step Agents: Finding Every Tool a Task Needs
Oct 16, 2026Sam Partee, River Stevens, Thierry Damiba +1Tool Selection for Language Model Agents Tool Discovery for LLM Agents
Explore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool Libraries
Oct 15, 2026Anna Mazhar, Seiji Maekawa, Nikita BhutaniTool Discovery for LLM Agents Tool-Using Agents
Cornell University · Megagon Labs
Plan or Retrieve First? A Scale Study of Tool Discovery Architectures
Oct 13, 2026Mingfei Zhang, Amit Surana, Ziauddin Majid +2Tool Discovery for LLM Agents LLM Tool Use
Apple
TaReD: Tool-Aware Recursive Decomposition for Long-Horizon Tasks
Oct 8, 2026Wei-Xiang Mao, Zhi-Kai Chen, De-Chuan Zhan +1Tool Discovery for LLM Agents Tool-Using Agents
Nanjing University, China · National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
Toollery: Scaling LLM Agents to Thousands of Skills and Tools
Sep 2, 2026Xiangxi Tian, Ran GuanTool Selection for Language Model Agents LLM Agent Skill Retrieval
Laboratories, Huawei
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation
Aug 25, 2026Olympia Saha, Amy Wang, Srinivasan ManoharanTool Discovery for LLM Agents LLM Tool Use
PayPal, Inc.
CacheRouter: A Dual-Path Tool Routing Architecture with Cache-Preserving Main-Model Isolation for Long-Tail Tool Discovery
Aug 24, 2026Donghui Zha, Lingwei Xu, Linxiao Wu +2Tool Discovery for LLM Agents LLM Tool Use
School of Mathematical Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China · School of Mathematics and Statistics, Chongqing University, Chongqing 401331, China · School of Science, Beijing Forestry University, Beijing 100083, China
Lomekwi: Resource-Bounded Tool Discovery in LLM Agents
Jul 18, 2026Roshan Klein-Seetharaman, Daniel Wang, Andrew XuTool Discovery for LLM Agents Language Model Scaling Laws
Sea12 Technologies · Yale University
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
Jul 6, 2026Zhiheng Xi, Dingwen Yang, Jiaqi Liu +22Tool Discovery for LLM Agents LLM Agent Evaluation
Fudan University · Zhejiang University · Shanghaijiaotong University +1
PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems
Jun 21, 2026Jiayu Liu, Qihan Lin, Cheng Qian +8Tool Discovery for LLM Agents Long-Horizon Agent Evaluation
University of Illinois Urbana-Champaign
SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents
Jun 15, 2026Qiao Xiao, Haochen Shi, Yisen Gao +9Tool Discovery for LLM Agents Agentic Retrieval
1Cornell University · 2The Hong Kong University of Science and Technology · 3The Ohio State University +1
ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
Jun 4, 2026Ashutosh Hathidara, Sai Shruthi Sistla, Sebastian Schreiber +1Tool-Augmented Language Model Agents LLM Auditing
SAP Labs
Indexing the Unreadable: LLM-Native Recursive Construction and Search of Service Taxonomies
May 28, 2026Wei Zheng, Yang Yan, Yiyang Shao +6LLM Agents Information Retrieval
openJiuwen A2X
FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
May 4, 2026Kyle Zheng, Han Zhang, Renliang Sun +2Tool-Use Evaluation Tool-Augmented Language Model Agents
UCLA
Agent-Facing Information Design in LLM Tool Registries: A Preregistered Test of Rhetoric, Position and Structure
Apr 12, 2026Haochuan Kevin Wang, Zechen ZhangTool-Use Evaluation Tool-Augmented Language Model Agents
Massachusetts Institute of Technology, Cambridge, MA, USA.
SkillGraph: Graph Foundation Priors for LLM Agent Tool Sequence Recommendation
Apr 7, 2026Hao Liu, Dongyu LiTool-Using Language Agents Tool Selection for Language Model Agents

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Tool Recommendation for Multi-Step Agents: Finding Every Tool a Task Needs

Sam Partee, River Stevens, Thierry Damiba, Valerie Fanelle

Tool Selection for Language Model Agents Tool Discovery for LLM Agents LLM Agent Efficiency

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A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on the platform we study have definitions that together exceed the context window of any model we evaluate. We present Tool Recommendation, a deployed system that gives an agent all of them, hundreds of times more than a typical MCP server exposes, through a fixed interface of three tools. It runs at the gateway between agent and tools, executing every call under the end user's own credentials and checking it against the user's consent for each service and the tools the agent may use. We evaluate it on both a public retrieval benchmark and billed end-to-end session cost. On ToolRet, Tool Recommendation places every tool a query needs in the top ten 57% more often than BM25, a lexical ranking that commercial tool search offers, and compares favorably with published systems up to nine times its size. In paired multi-step workflows, its first recommendation covered every required tool in 93% of Codex sessions, and against a fixed list of 100 tools it cut input tokens by 17% with Codex and 40% with Claude Code and the total bill in all 14 runs, by up to 40%, with no change in task completion.