S3 MCP Server

The S3 MCP server bridges the gap between AI assistants and Amazon S3, giving Claude or other agents a seamless way to manipulate cloud storage directly from conversational prompts. Instead of writing low‑level SDK calls or managing AWS credentials manually, developers can expose a set of declarative tools that the assistant can invoke. This reduces friction in data‑centric workflows, allowing agents to fetch, store, and manage artifacts without leaving the chat environment.

At its core, the server offers a comprehensive S3 CRUD interface: listing buckets, creating and deleting them, enumerating objects, uploading local files, downloading to disk, and handling presigned URLs for secure temporary access. Beyond basic file operations, it also manages configuration layers that are critical to production workloads—bucket policies, lifecycle rules, CORS settings, and object tagging. These capabilities enable the assistant to enforce compliance (e.g., block public access), automate data retention, or set cross‑origin permissions all within a single conversational turn.

Key features include:

• Declarative bucket and object management – Create, list, or delete buckets; upload, download, copy, and delete objects.
• Security tooling – Generate presigned URLs for time‑bound access; set or retrieve bucket policies to control permissions.
• Lifecycle & tagging – Define rules that transition or expire objects automatically, and attach metadata tags for cost‑analysis or governance.
• CORS configuration – Manage cross‑origin resource sharing rules, essential for web applications that fetch assets from S3.

Typical use cases span data pipelines, content delivery, and AI training. For example, an agent can ingest a new dataset from a local file, upload it to S3, tag it with project identifiers, and then trigger downstream processing steps—all from a single chat. In web‑app deployment scenarios, the assistant can set CORS rules and generate presigned URLs so that client browsers can securely upload images directly to storage without exposing credentials.

Integrating the server into an AI workflow is straightforward: developers add the MCP endpoint to their client configuration, and the assistant automatically routes relevant tool calls through it. The server’s API surface mirrors standard S3 operations, so existing AWS knowledge translates directly into conversational commands. This tight coupling not only accelerates development but also ensures that best practices—such as using presigned URLs or enforcing block‑public policies—are baked into the assistant’s behavior.