Scalable LLM Agent Tool Access in the Cloud
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
LLM agents increasingly rely on tool calling to act on external systems, and the Model Context Protocol (MCP) has quickly become its de facto interface. Operating MCP at cloud scale, however, becomes difficult. On the tool provider side, legacy services are not directly callable through MCP; the rapid protocol development also creates ongoing compatibility cost. On the agent side, the number of accessible tool is limited by the LLM context window and inference overhead; mounting a large tool set increases token usage and inference latency and can reduce task success rate. Moreover, for stateful MCP backends with multiple replicas, preserving session affinity increases client-side complexity. We present a cloud-scale gateway system for MCP service. It breaks the direct-connect model on the data plane and offloads legacy service integration, consolidating incompatible MCP variants, access control, tool recommendation, and session-aware routing to the gateway. Hybrid retrieval sustains 98% Top-15 recall; it scales agent tool access to 3,000+ with high tool selection accuracy, and reduces tool selection time by 8.9× and token usage by 23.8×, with low per-call overhead, stable under scale-out. Finally, we share the lessons learned from deploying the gateway system in production.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 23 of its 55 references that are in the map. First posted on arXiv 17 Jul 2026.
Builds on
23 of 55 references.
Closest ancestors · 2
Same direction, newest first
- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
- 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb
Other references · 21
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- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
- 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,107
- 2024AgentScope: A Flexible yet Robust Multi-Agent Platformcited by 120
- 2024Parrot: Efficient Serving of LLM-based Applications with Semantic Variablecited by 96
- 2025A Survey of AI Agent Protocolscited by 77
- 2024NetLLM: Adapting Large Language Models for Networkingcited by 76
- 2025Autellix: An Efficient Serving Engine for LLM Agents as General Programscited by 57
- 2025LLM Agents Making Agent Toolscited by 45
- 2024Teola: Towards End-to-End Optimization of LLM-based Applicationscited by 32
9 more
- 2023Agent-OM: Leveraging LLM Agents for Ontology Matchingcited by 26
- 2025AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applicationscited by 16
- 2025TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applicationscited by 16
- 2025Pie: A Programmable Serving System for Emerging LLM Applicationscited by 15
- 2025A Measurement Study of Model Context Protocol Ecosystemcited by 13
- 2025Kairos: Low-latency Multi-Agent Serving with Shared LLMs and Excessive Loads in the Public Cloudcited by 11
- 2025Cortex: Achieving Low-Latency, Cost-Efficient Remote Data Access For LLM via Semantic-Aware Knowledge Cachingcited by 4
- 2023gDoc: Automatic Generation of Structured API Documentationcited by 2
- 2023Improving API Documentation Comprehensibility via Continuous Optimization and Multilingual SDKcited by 1
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