filesystem

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Overview

The filesystem-mcp-server is a Model Context Protocol (MCP) server that gives AI assistants direct, structured access to the local filesystem. It allows AI-driven workflows to read, write, and organize files and directories as part of a larger reasoning process, making persistent storage and file-based context a first-class capability inside MCP-compatible assistants.

This server is commonly used for developer workflows, data preparation, note-taking, artifact generation, and any scenario where AI needs to work with local files in a controlled way.

Transport

stdio

Tools

  • read_file
  • read_multiple_files
  • write_file
  • edit_file
  • create_directory
  • list_directory
  • directory_tree
  • move_file
  • search_files
  • get_file_info
  • list_allowed_directories

Key Capabilities

  • Persistent local storage — Enable AI workflows to save outputs, intermediate results, and artifacts to disk.
  • File inspection and organization — Browse directories and reason about file structure and contents.
  • Artifact generation — Create code files, documents, configuration files, or datasets programmatically.
  • Workflow continuity — Support multi-step processes that rely on files across turns or sessions.
  • Controlled access model — Limit filesystem interaction to configured paths for safety and predictability.

How It Works

Check out our Filesystem MCP server guide.

The filesystem-mcp-server runs as a local MCP service and is configured with one or more allowed root directories that define the scope of access. AI clients connect to the server over the MCP protocol and issue requests to interact with files and folders within those boundaries.

The server mediates all filesystem interactions, ensuring paths are resolved safely and access stays within the configured scope. File contents and directory information are returned in structured formats that AI assistants can reason over, transform, or combine with outputs from other MCP servers.

By isolating filesystem access behind MCP, this design allows AI workflows to safely persist state, generate artifacts, and interact with local data — without embedding direct file I/O logic in the client or assistant itself.