Filesystem MCP Server

The Philgei MCP Server Filesystem is a lightweight, educational implementation of Claude’s original filesystem‑based Model Context Protocol (MCP) server. It enables AI assistants such as Claude to access a user‑specified set of local directories, treating those folders like an external data source that the assistant can read from and write to during a conversation. By exposing file system contents through MCP, developers gain a powerful way to augment the assistant’s knowledge base with real‑world documents, code snippets, or configuration files that reside on the user’s machine.

At its core, the server accepts a list of directory paths as command‑line arguments and validates each one to prevent accidental or malicious traversal outside the allowed scope. Once started, it registers a collection of MCP resources and tools that let the assistant query file listings, read file contents, or create new files. This tight integration means that a user can ask the assistant to “summarize all markdown notes in my Documents folder” or “open the file on my desktop,” and the assistant will retrieve that information in real time, without requiring any manual file transfers or API keys.

Key capabilities include:

• Secure path whitelisting: Only directories explicitly passed to the server are exposed, ensuring that sensitive system folders remain inaccessible.
• Dynamic resource discovery: The assistant can enumerate subdirectories and files on demand, enabling exploratory workflows such as searching for specific logs or data sets.
• Read‑only and write support: Depending on the underlying file system permissions, the assistant can not only read but also create or modify files, facilitating tasks like auto‑generating report drafts or updating configuration templates.
• Cross‑platform compatibility: The server is written in Python and relies on standard libraries, making it operable on Windows, macOS, and Linux as long as the MCP client (e.g., Claude Desktop) can launch it.

Typical use cases span from software development to data analysis. A developer might integrate the server into their local IDE workflow, allowing Claude to browse project files, suggest refactorings, or generate documentation directly from source code. A data scientist could let the assistant sift through experiment logs stored in a designated folder, summarizing results or flagging anomalies. Even non‑technical users benefit by delegating routine file management tasks—such as locating receipts, compiling a photo album, or backing up documents—to an AI that understands the context of their workspace.

Because the server is a proof‑of‑concept, it deliberately eschews production‑grade features like authentication tokens or network exposure. However, its straightforward configuration—adding a few paths to a JSON file and restarting the client—demonstrates how MCP servers can be extended beyond simple web APIs. Developers looking to prototype custom integrations or experiment with file‑based contexts will find this server a valuable sandbox for exploring the full potential of Model Context Protocol in real‑world scenarios.