OpenDAL MCP Server

Model Context Protocol Server for Apache OpenDAL™

The mcp-server-opendal server brings the power of Apache OpenDAL to the Model Context Protocol ecosystem, allowing AI assistants such as Claude to interact seamlessly with a wide range of cloud and on‑premises storage backends. By exposing a unified set of tools—listing, reading, and detecting file types—developers can give their assistants instant access to data stored in S3, Azure Blob Storage, Google Cloud Storage, and many other providers without writing custom adapters for each service.

At its core, the server translates MCP requests into OpenDAL operations. When a user invokes the or tool, the server resolves the provided URL (e.g., ) against environment‑defined backends, performs the operation through OpenDAL’s abstraction layer, and returns the result in a format that Claude can consume. The automatic text/binary detection feature means the assistant can decide whether to display a file’s contents as plain text or treat it as binary data, simplifying workflows that involve code snippets, configuration files, or large media assets.

Key capabilities include:

• Multi‑cloud support: Configure dozens of storage services via environment variables, each with its own alias and connection parameters.
• Unified API: A single set of MCP tools (, ) works across all backends, eliminating the need for provider‑specific logic in assistant code.
• Environment‑driven configuration: Store credentials and endpoint details securely outside the codebase, making deployments in CI/CD pipelines or containerized environments straightforward.
• Automatic type inference: The server detects whether a file is text or binary, enabling the assistant to handle files appropriately without additional metadata.

Typical use cases are plentiful. In a data‑science workflow, an assistant can list datasets stored in an S3 bucket, read sample CSV files, and even preview images from Azure Blob Storage—all within a single conversation. For developers, the server can fetch configuration files from Google Cloud Storage to initialize projects or retrieve code snippets from a private repository hosted on any supported backend. The ability to read arbitrary files directly means fewer context‑switches and a more fluid interaction between the user, the assistant, and remote data sources.

Because the server is built on OpenDAL, it inherits performance optimizations such as connection pooling and efficient streaming. Developers benefit from a single dependency that handles authentication, retry logic, and data format handling across providers. This reduces boilerplate code, minimizes security risks by centralizing credential management, and ensures that AI assistants remain agnostic to the underlying storage technology.