# Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval

arXiv: https://arxiv.org/abs/2408.01875 · HTML: https://mlsanity.com/paper/2408.01875/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 28 of 28)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [Large Language Models as Tool Makers](https://mlsanity.com/paper/2305.17126/) (2023)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://mlsanity.com/paper/2305.11554/) (2023)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use](https://mlsanity.com/paper/2310.03128/) (2023)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction](https://mlsanity.com/paper/2401.06201/) (2024)
- [Synthetic QA Corpora Generation with Roundtrip Consistency](https://mlsanity.com/paper/1906.05416/) (2019)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search](https://mlsanity.com/paper/2303.06573/) (2023)
- [Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents](https://mlsanity.com/paper/2402.09205/) (2024)
- [Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting](https://mlsanity.com/paper/2310.09716/) (2023)
- [Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum](https://mlsanity.com/paper/2308.14034/) (2023)
- [TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems](https://mlsanity.com/paper/2311.11315/) (2023)
- [Large Language Models are Strong Zero-Shot Retriever](https://mlsanity.com/paper/2304.14233/) (2023)
- [Context Aware Query Rewriting for Text Rankers using LLM](https://mlsanity.com/paper/2308.16753/) (2023)
- [Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval](https://mlsanity.com/paper/2308.08285/) (2023)

## Built on by (most cited first, 30 of 52)

- [ToolGen: Unified Tool Retrieval and Calling via Generation](https://mlsanity.com/paper/2410.03439/) (2024)
- [Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs](https://mlsanity.com/paper/2507.09477/) (2025)
- [Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation](https://mlsanity.com/paper/2503.07826/) (2025)
- [ScaleMCP: Dynamic and Auto-Synchronizing Model Context Protocol Tools for LLM Agents](https://mlsanity.com/paper/2505.06416/) (2025)
- [MCP-Zero: Active Tool Discovery for Autonomous LLM Agents](https://mlsanity.com/paper/2506.01056/) (2025)
- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
- [Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases](https://mlsanity.com/paper/2410.14594/) (2024)
- [MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations](https://mlsanity.com/paper/2507.21428/) (2025)
- [ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering](https://mlsanity.com/paper/2510.20036/) (2025)
- [Don't Break the Cache: An Evaluation of Prompt Caching for Long-Horizon Agentic Tasks](https://mlsanity.com/paper/2601.06007/) (2026)
- [SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale](https://mlsanity.com/paper/2606.03056/) (2026)
- [Graph RAG-Tool Fusion](https://mlsanity.com/paper/2502.07223/) (2025)
- [Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems](https://mlsanity.com/paper/2511.01854/) (2025)
- [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026)
- [BEDI: A Comprehensive Benchmark for Evaluating Embodied Agents on UAVs](https://mlsanity.com/paper/2505.18229/) (2025)
- [Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use](https://mlsanity.com/paper/2602.23368/) (2025)
- [SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Execution](https://mlsanity.com/paper/2605.10114/) (2026)
- [Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists](https://mlsanity.com/paper/2506.00042/) (2025)
- [ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution](https://mlsanity.com/paper/2604.13787/) (2026)
- [TURA: Tool-Augmented Unified Retrieval Agent for AI Search](https://mlsanity.com/paper/2508.04604/) (2025)
- [Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems](https://mlsanity.com/paper/2511.16654/) (2025)
- [Agent-as-a-Graph: Knowledge Graph-Based Tool and Agent Retrieval for LLM Multi-Agent Systems](https://mlsanity.com/paper/2511.18194/) (2025)
- [SkillSight: Calibrating Generic Content Bias for Skill Retrieval](https://mlsanity.com/paper/2607.18785/) (2026)
- [Task-Aligned Tool Recommendation for Large Language Models](https://mlsanity.com/paper/2411.09613/) (2024)
- [GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks](https://mlsanity.com/paper/2502.14848/) (2025)
- [LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval](https://mlsanity.com/paper/2508.07690/) (2025)
- [Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning](https://mlsanity.com/paper/2601.07782/) (2026)
- [RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents](https://mlsanity.com/paper/2605.13391/) (2026)
- [Multi-Field Tool Retrieval](https://mlsanity.com/paper/2602.05366/) (2026)
- [Risk-Aware Reranking for Agentic Tool Retrieval](https://mlsanity.com/paper/2608.22751/) (2026)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have enabled autonomous agents with complex reasoning and task-fulfillment capabilities using a wide range of tools. However, effectively identifying the most relevant tools for a given task becomes a key bottleneck as the toolset size grows, hindering reliable tool utilization. To address this, we introduce Re-Invoke, an unsupervised tool retrieval method designed to scale effectively to large toolsets without training. Specifically, we first generate a diverse set of synthetic queries that comprehensively cover different aspects of the query space associated with each tool document during the tool indexing phase. Second, we leverage LLM's query understanding capabilities to extract key tool-related context and underlying intents from user queries during the inference phase. Finally, we employ a novel multi-view similarity ranking strategy based on intents to pinpoint the most relevant tools for each query. Our evaluation demonstrates that Re-Invoke significantly outperforms state-of-the-art alternatives in both single-tool and multi-tool scenarios, all within a fully unsupervised setting. Notably, on the ToolE datasets, we achieve a 20% relative improvement in nDCG@5 for single-tool retrieval and a 39% improvement for multi-tool retrieval.