"Filen" matching MCP servers:

Provides a human-in-the-loop security layer for AI agents by intercepting file operations, explaining them with a local LLM, and enforcing a deterministic policy that requires user approval for risky actions.

Enables AI agents to safely read, write, and search files within a whitelisted sandbox directory, rejecting any attempts to escape the sandbox.

Enables MCP clients to manage files in an isolated workspace and perform lightweight Git operations through a protected Streamable HTTP endpoint, including listing, reading, writing, searching, replacing, and deleting files plus checking status and diffs.

Enables read-only listing and reading of files in an Azure Files SMB share, enforcing each user's NTFS permissions per request.

Enables agents to securely stage, validate, and submit binary data for on-premises storage, then list, inspect, process via registered server-side processors, and token-guarded delete those assets using opaque identifiers. Upload bytes travel over a separate data plane rather than through MCP JSON, with staging, validation, and prepare/apply/verify flows enforced throughout.

Read selected local folders from ChatGPT on macOS, with per-folder read-only or read-write access and hash-checked, recoverable edits. Requires local installation and a private ChatGPT app. The Glama container is a synthetic evaluation demo; it does not connect to files on your Mac.

Enables read-only auditing of repositories by scanning actual files, flagging risky code, secrets, and untrusted platform configs, and generating Chinese-language reports for human decision-making.

Enables AI assistants to list directories, read, write, and search files within a locked root directory, using resolve-then-validate path checks, size caps, and binary detection to prevent sandbox escapes.

Enables an AI host to list, read, and move files within a single designated folder, where each action is held until a human approves it through a consent card and the decision is recorded on a tamper-evident chain. Actions proceed only with a written receipt, and any missing answer or error counts as a refusal.

Enables Large Language Models to safely browse and interact with local file systems through secure directory listing, file reading, and content search capabilities. Built with comprehensive security controls and high-performance handling of large directories and files.

Provides a hardened filesystem interface that lets a local model read and write files only within user-specified directories, with symlink protection and atomic operations.

Enables safe file system operations including reading, writing, updating, and deleting files with built-in security safeguards, automatic backups, and comprehensive error handling. Provides directory listing, file metadata extraction, and protects against operations on system-critical paths.