Unstorage MCP Server

Overview

Unstorage MCP Server is a lightweight, extensible key‑value storage service that bridges the gap between AI assistants and persistent data stores. Built on top of the popular unjs/unstorage library, it abstracts away the intricacies of various storage backends—memory, filesystem, Redis, HTTP‑based Unstorage servers, and MongoDB—providing a unified interface for Claude or other MCP‑compatible assistants. By exposing a rich set of tools such as , , and metadata operations, the server allows conversational agents to read from, write to, and manage data seamlessly during a dialogue.

The core value proposition lies in its transport flexibility. Unstorage MCP can be run over standard I/O, making it ideal for local or sandboxed environments, or via HTTP with support for SSE and streamable responses, enabling real‑time updates in web applications. This duality ensures that developers can integrate the server into a wide range of workflows, from command‑line debugging to production APIs without changing client code.

Key features include:

• Multi‑driver support: Choose the most appropriate backend for your use case—fast in‑memory caches, durable filesystem snapshots, scalable Redis clusters, or document stores like MongoDB.
• Mount‑point abstraction: Each driver can be mounted under a distinct namespace, allowing fine‑grained control over data isolation and routing.
• Comprehensive toolset: The server offers CRUD operations, bulk fetches (), metadata handling (, ), and key enumeration () out of the box.
• Secure configuration: Environment variables expose connection strings, base paths, and HTTP headers, keeping secrets out of code.
• Optional modification lock: The flag turns the server into a read‑only endpoint, useful for audit scenarios or data sharing without write access.

Typical use cases span across several domains:

• AI‑driven development assistants: Store and retrieve code snippets, configuration files, or stateful information during a coding session.
• Chatbot personalization: Persist user preferences or conversation history in Redis or MongoDB while keeping a local cache for low‑latency access.
• Data pipelines: Use the server as an intermediary between a Claude model and downstream services, enabling the model to orchestrate complex workflows by reading from or writing to a shared key‑value store.
• Testing and debugging: Run the server locally via stdio to simulate persistence without deploying external services, then switch to HTTP for integration tests.

By integrating Unstorage MCP into an AI workflow, developers gain a single source of truth that is both highly configurable and easy to expose to assistants. The server’s design encourages modularity—new drivers can be added, or existing ones replaced without altering the client logic—making it a robust foundation for building intelligent, stateful applications.