Serverless MCP server for AI agent data lake access on Amazon ECS Fargate
AWS Machine Learning Blog · 人工智能
Organizations building applications powered by artificial intelligence (AI) face a recurring challenge: giving large language model (LLM) agents access to enterprise data lakes without duplicating integration logic across every agent and application. Most teams begin by wiring agents directly to databases and APIs, with no shared gateway such as a Model Context Protocol (MCP) server in between. Each new dataset demands custom code, and each agent reimplements its own access control. As the number of datasets or agents grows, permission enforcement drifts, and onboarding a new data source becomes a multi-day engineering effort. Our team supports Amazon’s North America Sort Center (NASC) network, where planning teams rely on operational data to manage capacity, labor, and package flow. To give those teams conversational access to that data, we built a serverless Model Context Protocol (MCP) server on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to address this problem for planning teams in NASC. The server acts as a centralized, agent-agnostic gateway between AI agents and our enterprise data lake. Exposing a new dataset requires only a JSON metadata file that the server discovers automatically on the next pipeline sync. In this post, we describe the architecture, show how a metadata-driven approach removes the need for per-dataset integration code, and explain how the serverless infrastructure supports multiple agents and applications in a multi-tenant environment. Problem statement and design goals Two interrelated challenges shaped how we designed this system: fragmented data access patterns and an unsustainable onboarding model. Integration challenge at scale Our data lake contains thousands of datasets spanning operational metrics, business analytics, and domain-specific data. Multiple teams wanted AI assistants that could query this data conversationally, but each built its own integration from scratch. Each agent embedded its own SQL generatio