Filesystem MCP Server

The Filesystem MCP Server is a lightweight Ruby implementation of the Model Context Protocol that exposes direct access to a filesystem for AI assistants such as Claude. By running this server, developers can let an assistant read, write, edit, and organize files on the host machine in a controlled, secure manner. The server is especially useful for building data‑driven workflows where an AI needs to inspect code, documents, or configuration files and then modify them based on user intent.

This MCP server solves the common problem of bridging an AI’s conversational interface with low‑level file operations. Instead of writing custom integrations or relying on third‑party services, developers can launch a single Ruby process that listens for MCP requests and performs filesystem actions within the boundaries defined by command‑line arguments. The result is a predictable, auditable interaction that keeps the assistant’s actions confined to designated directories.

Key capabilities include:

• File operations: read, write, edit (with diff preview), and move files or directories.
• Directory management: create new folders recursively and list contents with optional metadata.
• Advanced search: locate files by pattern, filter by size or date, and sort results.
• Bulk metadata: retrieve information for many files in a single call, reducing round‑trips.
• Security controls: enforce MCP roots to prevent accidental access outside allowed paths, and optional API‑key authentication for sensitive environments.

Typical use cases involve:

• Code review assistants that can read source files, suggest edits, and apply changes directly to a repository.
• Documentation generators that pull content from markdown files, update them, and reorganize directories.
• Data pipelines where an AI pulls CSVs or logs, processes them, and writes results back to disk.
• Configuration management tools that let a user query system settings stored in files and update them through natural language commands.

Integration into an AI workflow is straightforward: the assistant sends a tool invocation with the desired file operation, the MCP server executes it, and returns the result or confirmation. Because the server runs locally, latency is minimal, and developers can monitor logs to audit every file access. Its reliance on the gem guarantees compatibility with existing MCP clients, while optional authentication adds a layer of protection for production deployments. Overall, the Filesystem MCP Server provides a robust, secure bridge between conversational AI and the underlying filesystem, enabling developers to build powerful, context‑aware applications without reinventing file handling logic.