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AWS 示範以 metadata JSON 擴充資料湖 MCP 伺服器，部署於 Amazon ECS Fargate
AWS 於 2026 年 11 月 6 日發布一套以 Amazon ECS 和 AWS Fargate 執行的無伺服器 Model Context Protocol (MCP) 伺服器架構，讓 AI 代理存取企業資料湖。新增資料集只需提供 metadata JSON；工具則涵蓋 SQL 查詢、語意搜尋與政策搜尋。
完整報導
AWS 介紹一套供 AI 代理存取企業資料湖的無伺服器 MCP 伺服器，建置於 Amazon ECS 和 AWS Fargate。架構分為 client、MCP server、tools 與 knowledge-base refresh 四層；任何 MCP-compatible client 都可透過標準化的 `/mcp` endpoint 探索並呼叫工具。
資料集的加入以 metadata JSON 檔案處理，不需要為每個資料集撰寫整合程式碼；新 metadata 會在下一次 pipeline sync 時自動探索。工具包含透過 Amazon Athena 和 Amazon Redshift 執行 SQL 查詢、透過 Amazon Bedrock Knowledge Bases 進行語意搜尋，以及政策搜尋。
安全設計方面，Amazon Bedrock Guardrails 套用於每次模型互動；Athena 工具只允許 SELECT 查詢，並拒絕寫入操作。MCP 伺服器位於 private subnets、部署在 internal Application Load Balancer 後方，無法從網際網路連線；client layer 則使用 Amazon Cognito 進行 JWT 驗證，並以 Amazon DynamoDB 儲存 session。
AWS 作者表示，測試中的端到端互動延遲為 15–20 秒。這項指南聚焦於資料湖存取；AWS 於 2026 年 4 月 14 日發布的另一份 MCP 指南，則示範由 Gradio UI、Amazon Bedrock AI agent 與 FastMCP server 組成的三服務應用，同樣部署於 Amazon ECS 和 AWS Fargate。
為什麼值得注意
這套設計把資料集加入流程設為 metadata JSON，並透過同一個 MCP endpoint 提供查詢與搜尋工具，讓讀者能評估其是否符合資料湖代理的整合需求。文章也明確列出網路隔離、驗證與查詢限制等控制方式，以及作者在測試中報告的互動延遲。
來源證據
先看主要證據，再查看佐證來源與來源脈絡。
主要證據
1Serverless MCP server for AI agent data lake access on Amazon ECS Fargate
Aishwarya Pawar
“Learn how to build a serverless Model Context Protocol (MCP) server on Amazon ECS with AWS Fargate that gives AI agents secure, metadata-driven access to an enterprise data lake. Exposing a new dataset requires only a JSON file, with no per-dataset integration code.”
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