arXiv:2608. 16502v1 Announce Type: new Abstract: Large-scale agents increasingly rely on retrieval to access external capabilities.
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arXiv:2608.21375v1 Announce Type: new Abstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph...
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.
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:2607. 02116v1 Announce Type: new Abstract: Autonomous AI agents increasingly depend on external knowledge stores, yet most retrieval pipelines provide relevance without durable guarantees of provenance, version identity, integrity, traceability, or point-in-time reconstruction.
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.
Cartograph is a federated Model Context Protocol (MCP) proxy that reduces AI agent tool discovery from linear catalog traversal to progressive disclosure, exposing only a few proxy tools instead of all definitions. It uses operator-attested capability cards, a three-layer confusable-cluster analysis called Rift, and a two-stage retrieval process to rank servers before tools. In a 22-server, 374-tool deployment, Cartograph achieves higher recall (R@5 = 0.816 vs. 0.592) and drastically fewer tokens (475 vs. 42,450) for discovery exchanges, with minimal latency overhead.