AWS S3 Model Context Protocol Server

Overview

The AWS S3 Model Context Protocol (MCP) server is a lightweight bridge that lets AI assistants—such as Claude—to pull data directly from Amazon S3 into their working context. By exposing S3 buckets, objects, and PDF files as resources and tools, the server removes the need for custom integration code, enabling assistants to query cloud storage as if it were a native data source. This is particularly valuable for developers who want to build AI‑powered workflows that include document retrieval, data analysis, or content generation from files stored in S3.

At its core, the server offers two types of capabilities:

• Resources: These act like read‑only GET endpoints that load content into the assistant’s context. Currently, only PDF documents are supported, with a limit of 1 000 objects per bucket to keep the payload manageable. When a resource is requested, the assistant receives the PDF data as part of its prompt, allowing it to reference or analyze the file directly.
• Tools: A set of actionable commands that interact with S3. The server implements , , and . These tools let the assistant enumerate buckets, list objects within a bucket, or fetch an arbitrary object by key. Because tools are invoked during runtime, they can be combined with prompts to drive dynamic workflows—e.g., “Find the latest report in bucket X and summarize its contents.”

The design follows MCP best practices: it uses standard JSON over stdio, supports authentication via AWS credentials configured in the default profile, and can be launched as a development or published server. Integration with Claude Desktop is straightforward—developers add the server to the configuration, and the assistant automatically discovers and can call its tools.

Real‑world scenarios that benefit from this server include:

• Document‑centric AI assistants: Pull PDFs from S3, load them into context, and let the assistant answer questions or extract insights.
• Data pipelines: Use to discover new data files, then to ingest them into a downstream processing workflow.
• Compliance and audit: Retrieve specific logs or policy documents stored in S3 for automated review.

Unique advantages of this MCP server are its minimal footprint and native support for S3’s object model, which means developers can rely on familiar AWS permissions and SDK semantics while keeping the integration lightweight. The server also enforces a strict object limit to prevent runaway payloads, making it safe for production use. Overall, the AWS S3 MCP server provides a seamless, standardized path for AI assistants to consume cloud‑stored data without custom plumbing.