ADLS2 MCP Server

The ADLS2 MCP server is a dedicated bridge that lets AI assistants—such as Claude—interact seamlessly with Azure Data Lake Storage Gen 2. By exposing a standardized set of file‑system and file operations over the Model Context Protocol, it removes the need for custom integrations or manual API calls when an assistant needs to read from or write to a cloud data lake. This capability is especially valuable for developers building AI‑driven analytics pipelines, data engineering tools, or any application that relies on large volumes of raw or processed data stored in ADLS2.

At its core, the server implements a collection of MCP tools that mirror common storage actions: listing filesystems, creating or deleting containers, uploading and downloading blobs, checking existence, renaming, and manipulating metadata. Each tool is described in plain language by the MCP specification, so an AI assistant can request a file operation without needing to understand low‑level Azure SDK calls. The server also handles authentication transparently—either via an explicit storage account key or through Azure CLI credentials—ensuring that developers can deploy it in both local and CI environments without hard‑coding secrets.

Key features include:

• Full filesystem CRUD: Create, list, and delete containers (filesystems) directly from the assistant.
• Rich file operations: Upload, download, rename, and verify files; retrieve properties or metadata; set multiple metadata entries in a single call.
• Directory management: Create, delete, and rename directories; enumerate all paths under a directory.
• Read‑only mode: A configurable flag that locks the server to non‑destructive operations, useful for audit or preview scenarios.
• Environment‑driven configuration: Paths for uploads/downloads, logging levels, and Azure credentials are all supplied via environment variables, keeping the server lightweight and secure.

Typical use cases include:

• Data science notebooks: An AI assistant can fetch raw datasets from ADLS2, suggest preprocessing steps, and write back cleaned data without the user writing any code.
• ETL orchestration: A workflow manager can invoke the MCP tools to stage files, trigger downstream jobs, or archive completed datasets.
• Compliance and auditing: In read‑only mode, auditors can let an AI explore data lake contents while preventing accidental modifications.
• Rapid prototyping: Developers can spin up the server locally, authenticate with Azure CLI, and immediately start testing assistant interactions against a real data lake.

Integrating the ADLS2 MCP server into an AI workflow is straightforward: add a new entry to the assistant’s configuration pointing to the server command, set the required environment variables, and restart. Once active, the assistant can issue any of the defined tools by name, passing parameters in JSON format. The server responds with structured results that the assistant can incorporate into its next response or prompt. This tight coupling between AI reasoning and cloud storage operations unlocks powerful, data‑centric conversational experiences that were previously cumbersome or impossible to implement.