Azure Blob Storage MCP Server

Overview

The MCP Azure Storage Sample provides a ready‑made Model Context Protocol server that exposes the full breadth of Azure Blob Storage functionality to AI assistants. By translating native Azure SDK calls into MCP resources and tools, the server allows conversational agents—such as Claude or GPT‑4o—to perform storage operations through natural language, without needing direct SDK knowledge. This bridges the gap between cloud storage services and generative AI workflows, enabling developers to build data‑centric assistants that can list containers, create or delete blobs, and manage uploads or downloads—all through a unified MCP interface.

At its core, the server implements asynchronous Python APIs that map to Azure Blob Storage actions: listing containers or blobs, creating new containers, uploading and downloading blob data, and deleting items. Authentication is handled via Microsoft Entra Managed Identity or Azure CLI credentials, ensuring secure access to tenant resources without hard‑coding secrets. The MCP server then publishes these capabilities as discoverable tools, each annotated with clear names and descriptions so that an AI client can request the exact operation it needs. This design keeps the server stateless, scalable, and compliant with MCP’s resource discovery model.

For developers building AI assistants, the sample includes a companion client that demonstrates how to consume these MCP tools. The client features an AI‑powered chat interface powered by Azure OpenAI GPT‑4o, automatically discovering the server’s tool set and translating user intents into concrete storage commands. This pattern shows how an assistant can ask, “Show me all blobs in the ‘logs’ container,” and receive a direct listing without any manual API calls. The client also illustrates how to handle authentication flows, error reporting, and streaming responses within a conversational context.

Key capabilities highlighted by the sample are:

• Full CRUD support for containers and blobs, enabling end‑to‑end data management.
• Asynchronous execution, allowing the assistant to remain responsive while long uploads or downloads complete in the background.
• Tool discovery via MCP, so new storage operations can be added to the server and instantly become available to any compliant client.
• Secure authentication using Managed Identities, eliminating credential leakage risks.

Typical use cases include:

• Data‑centric chatbots that can fetch logs, configuration files, or user uploads directly from storage.
• DevOps assistants that can create temporary containers for build artifacts or clean up stale blobs automatically.
• Data science pipelines where a conversational agent can trigger data ingestion, preprocessing, or model training by simply instructing the assistant to upload a dataset.

By encapsulating Azure Blob Storage behind MCP, developers gain a clean abstraction that lets AI assistants interact with cloud storage as if it were a native tool, while still leveraging the robustness and security of Azure’s platform. This sample serves as both a reference implementation and a launchpad for building more sophisticated AI‑driven data workflows.