Unified Filesystem Access

Unified Filesystem Access is an MCP server that provides AI assistants with secure, controlled access to the local filesystem, enabling file reading, writing, searching, and directory operations within explicitly configured permission boundaries. This MCP bridge allows language models to interact with files and directories on your machine while maintaining strict access controls through configurable directory allowlists that prevent unauthorized filesystem traversal.

When to Use This MCP Server

Connect this server when...

• You need AI assistants to read, write, and manage files on your local filesystem with controlled permissions
• Your workflow requires the AI to search for files, read configuration files, or process text documents on disk
• You want to give an AI assistant access to specific project directories while blocking access to sensitive areas
• You are building local automation workflows that involve creating, editing, or organizing files and directories
• You need the AI to analyze log files, configuration files, or data files stored on your local machine

Consider alternatives when...

• You only need to access files in cloud storage (S3, GCS, Azure Blob) which have their own MCP servers
• Your files are in a version-controlled repository where built-in git tools provide sufficient access
• You need database-level file access rather than operating system filesystem operations

Quick Start

Copy
# .mcp.json configuration
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/path/to/allowed/directory"
      ]
    }
  }
}

Connection setup:

1. Ensure Node.js 18+ is installed on your system
2. Determine which directories the AI should have access to
3. Add the configuration above to your .mcp.json, specifying allowed directory paths as arguments
4. Restart your MCP client to activate filesystem access

Example tool usage:

# Read a file
> Read the contents of config.yaml in the project directory

# Search for files
> Find all .json files in the data directory that were modified today

# Create a file
> Create a new file called output.txt with the analysis results

Core Concepts

Concept	Purpose	Details
Directory Allowlist	Access control	Only directories explicitly passed as arguments are accessible; all other paths are blocked
File Operations	Content management	Read, write, create, delete, and move files within allowed directories
Directory Operations	Structure management	List directory contents, create directories, and navigate the filesystem hierarchy
Search Capabilities	File discovery	Find files by name patterns, modification date, size, and content within allowed paths
Symlink Safety	Traversal prevention	Symbolic links that resolve outside allowed directories are blocked to prevent permission bypass

Architecture:

+------------------+       +------------------+       +------------------+
|  Local           |       |  Filesystem MCP  |       |  AI Assistant    |
|  Filesystem      |<----->|  Server (npx)    |<----->|  (Claude, etc.)  |
|  (allowed dirs)  | fs    |  stdio transport  | stdio |                  |
+------------------+       +------------------+       +------------------+
        |
        v
+------------------------------------------------------+
|  Read > Write > Search > Create > Delete > Move       |
+------------------------------------------------------+

Configuration

Parameter	Type	Default	Description
allowed_dirs	string[]	(required)	One or more directory paths passed as arguments that define the access boundary
read_only	boolean	false	Restrict the server to read-only file operations when set to true
follow_symlinks	boolean	false	Whether to follow symbolic links (only within allowed directories)
max_file_size	integer	10485760	Maximum file size in bytes that can be read or written (default 10MB)
include_hidden	boolean	false	Whether to include hidden files (dotfiles) in directory listings and searches

Best Practices

1. Grant the minimum necessary directory access. Only add directories that the AI genuinely needs. Avoid granting access to your home directory or root filesystem. Each allowed path should be a specific project directory, data folder, or output location.

2. Use read-only mode for analysis workflows. When the AI only needs to read files (log analysis, configuration review), enable read-only mode. This prevents accidental file modifications or deletions during exploratory operations.

3. Keep sensitive files outside allowed directories. Ensure directories containing credentials, private keys, or personal data are not in the allowlist. Structure projects so sensitive configuration is stored separately from working data directories.

4. Set appropriate file size limits. The default 10MB limit prevents the AI from reading extremely large files that would be slow to process. Adjust based on your actual file sizes, but be cautious about increasing dramatically.

5. Audit filesystem operations in your MCP logs. Monitor which files the AI reads and writes through your MCP server logs. Regular auditing ensures the AI accesses only expected files and reveals unexpected access patterns.

Common Issues

"Permission denied" when accessing files in allowed directories. The MCP server runs with the same OS permissions as the launching user. Verify your user account has read/write permissions on the target files. On Unix systems, check ownership and permissions with ls -la.

Symlink traversal blocked unexpectedly. The server blocks symbolic links resolving outside allowed directories. If you need to follow symlinks, ensure the target directory is also in the allowlist, or restructure to avoid cross-directory symlinks.

Large directory listings time out. Directories with thousands of files produce slow listings. Use search filters and specific subdirectory paths rather than listing entire large directories. The server may truncate results for very large directories.