Cloud Gateway Enables Scalable LLM Agent Tool Access

A new cloud-scale gateway system has been developed to address the challenges of enabling large language model (LLM) agents to access a vast array of external tools. The Model Context Protocol (MCP) is a de facto standard for tool calling, but scaling it in the cloud presents difficulties. Tool providers face integration costs with legacy services and rapid protocol changes, while agents are limited by context window size and inference overhead when managing large toolsets. This gateway system breaks the direct-connect model for data plane operations, centralizing critical functions. It offloads legacy service integration, consolidates incompatible MCP variants, manages access control, provides tool recommendations, and handles session-aware routing. This consolidation allows agents to access over 3,000 tools with high selection accuracy (98% Top-15 recall) while drastically reducing tool selection time by 8.9 times and token usage by 23.8 times. The system maintains low per-call overhead and stable performance even under scale-out conditions. The paper also shares valuable lessons learned from its production deployment, demonstrating a practical solution for scalable and efficient LLM agent tool access in cloud environments.