The paper evaluates six retrieval structures for multi‑tool requests in LLM agents, measuring how many required tools appear in the top‑k results and the associated LLM calls, tokens, and latency. Across two testbeds—345 AppWorld queries and 103 MCP‑Bench tasks—none of the untrained, single‑LLM‑call structures completes more than 10% of AppWorld queries at k = 16. A trained set‑level retriever and an LLM‑navigated hierarchy achieve higher completeness (up to 58% on MCP‑Bench) but at different costs and failure modes, with complete retrieval sets leading to 100% task success in a downstream AppWorld agent test.
arXiv:2605. 24660v2 Announce Type: replace-cross Abstract: Before an LLM agent can use a tool, a retrieval system must decide which candidate tools to show to the agent.
arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.
arXiv:2608. 16502v1 Announce Type: new Abstract: Large-scale agents increasingly rely on retrieval to access external capabilities.
arXiv:2610.23707v1 Announce Type: new Abstract: 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 paper introduces Enrich‑Retrieve‑Rank, a scalable method for discovering capabilities in large agent ecosystems. It replaces in‑context routing with an offline enrichment step that converts sparse metadata into searchable profiles, followed by an online retrieve‑then‑rank pipeline that returns a ranked shortlist without invoking candidates. Experiments show that as the number of capabilities grows from 10 to 7,278, the new approach maintains higher top‑1 accuracy and reduces cost by 70× compared to full‑context baselines.
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.
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.
The paper investigates how adding past usage examples of tools to a shared tool index—called folding—affects the retrieval of tools that an AI agent has never used. While folding improves retrieval for tools with examples, it significantly reduces the likelihood that an unused tool appears in the top five results on two of three evaluated catalogs, dropping from 73% to 40% in one benchmark. The authors propose a new scoring method, ZoE, which separates documentation and usage scores, and show that ZoE retrieves unused tools more often than a simple router or a constant‑boost rule, suggesting that evaluations of pooled agent experience should distinguish between used and unused tools.
arXiv:2606. 28376v1 Announce Type: cross Abstract: Long-horizon large language model (LLM) agents accumulate interaction trajectories that quickly exceed any practical prompt budget, and existing memory methods either truncate aggressively and lose non-local evidence or retain boilerplate that degrades decision quality.
arXiv:2606. 12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck.
arXiv:2608. 12282v1 Announce Type: new Abstract: Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities in isolation.
The paper introduces RAGFlow, a retrieval‑augmented generation framework that transforms an undisciplined query loop into a structured pipeline of five stages. It emphasizes knowledge compilation—enriching documents with navigation hierarchies, claim‑evidence pairs, entity graphs, timelines, mind maps, ontologies, wikis, and memory—rather than improving retrieval accuracy. The compiled context reduces query tokens by 91% when evidence suffices, cuts latency from 140 s to 30 s, and provides governance and integration benefits not captured by standard accuracy benchmarks.