How Amazon Built a Serverless MCP Gateway for Data Lake Access
Serverless MCP server for AI agent data lake access on Amazon ECS Fargate
- Who
- AWS engineers
- What
- describe a serverless MCP server architecture for AI agents to access enterprise data lakes
What happened
Amazon engineers describe a serverless Model Context Protocol server on Amazon ECS Fargate that gives AI agents a shared gateway to an enterprise data lake. Adding a dataset requires a metadata JSON file, which a pipeline indexes into Amazon Bedrock Knowledge Bases; optional context mappings narrow the tables an agent searches. In testing, the scoped end-to-end interaction took 15–20 seconds.
Why it matters
The post explains how JSON metadata and context mappings connect new datasets to a shared MCP gateway, with a reported 15–20 second end-to-end interaction time in testing.
Read next
See the full storyAWS Engineers Describe a Serverless MCP Gateway for Data Lake AccessThe story so far
AWS engineers described a serverless Model Context Protocol (MCP) server on Amazon ECS Fargate that gives AI agents a shared gateway to an enterprise data lake. The architecture uses Amazon Bedrock Knowledge Bases to index dataset metadata, avoiding separate integration code for each dataset. To add a dataset, teams provide a metadata JSON file and can optionally add a context mapping that narrows which tables an agent searches. In testing, a scoped end-to-end interaction took 15–20 seconds.
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