Filestash

Overview

The Filestash MCP server turns a sophisticated, web‑based file manager into an AI‑ready interface for managing data across a broad spectrum of storage backends. It solves the problem of fragmented file access by presenting a single, unified view that can reach FTP, SFTP, WebDAV, SMB, cloud providers (Dropbox, Google Drive, Backblaze B2), object stores (S3, Minio, Storj), and even database or LDAP directories. For developers building AI assistants that need to read, write, or manipulate files in diverse environments, this server eliminates the need for custom connectors and provides a consistent API surface.

At its core, Filestash exposes RESTful endpoints for common file operations—listing directories, downloading and uploading files, creating folders, and deleting items. These endpoints are wrapped in the Model Context Protocol (MCP), allowing an AI client to request actions with a simple JSON payload and receive structured responses. The MCP integration is designed to be lightweight, making it trivial for a Claude or other LLM agent to issue “list this folder” or “upload a CSV to S3” commands without leaving the conversation. The server’s modular architecture means that each backend implements a common interface, so adding support for new protocols is as simple as dropping in another plugin.

Key capabilities include:

• Multi‑protocol access: From legacy FTP servers to modern cloud APIs, all reachable through the same UI and API.
• Rich media handling: Built‑in viewers for music, video, and images, with optional transcoding and Chromecast support, enabling an AI to preview assets before acting on them.
• Shared links & network mounts: Files can be shared via public URLs or mounted locally as network drives, allowing an AI to reference data stored in disparate locations.
• Extensibility: A plugin ecosystem lets developers add new backends, authentication methods, or UI themes without touching core code.
• MCP and LLM hooks: The API is documented to work seamlessly with LLMs, exposing higher‑level actions such as “search files by metadata” or “sync a folder to another backend”.

Use Cases

• AI‑powered data ingestion: A chatbot can pull datasets from an S3 bucket, process them with a model, and store results back to FTP or Google Drive—all through MCP calls.
• Cross‑cloud file management: Enterprises with hybrid storage can let an assistant move files between on‑prem SMB shares and cloud buckets without manual intervention.
• Automated backups: An AI agent can schedule incremental uploads of local directories to a remote backup service, leveraging the server’s native support for SFTP and Backblaze B2.
• Contextual media retrieval: A virtual assistant can play a requested song by locating it in a shared music library, streaming it via the built‑in viewer.

Integration with AI Workflows

Developers embed the Filestash MCP server into their AI stack by pointing the LLM’s tool‑calling endpoint at the server’s route. Because the protocol follows a simple request/response pattern, the AI can compose complex file‑based instructions—such as “copy this image from S3 to an FTP server, then share a link”—and receive immediate confirmation. The modular plugin system also allows the AI to discover new capabilities at runtime, adapting its behavior as new backends are added.

Unique Advantages

• One‑stop file manager: Unlike generic APIs that target a single provider, Filestash covers dozens of protocols in one package.
• Browser‑first design: The same UI that developers use to verify file operations is available to end users, ensuring transparency and auditability.
• Open‑source plugin model: Contributions such as new authentication schemes or UI themes can be shared across the community, keeping the core lightweight.
• Low overhead: Running as a Docker container or native binary, it can be deployed in edge environments where an AI assistant might reside.

In summary, the Filestash MCP server equips AI assistants with a powerful, unified file access layer that spans traditional and modern storage systems. Its plugin‑driven architecture, media capabilities, and LLM‑friendly API make it an indispensable tool for developers looking to weave file manipulation into conversational AI workflows.