Enables extraction of metadata from HWP and HWPX files (Korean word processor formats) without requiring HWP installation. Supports document info, statistics, security, and font details.
A Model Context Protocol server that provides intelligent file reading and semantic search capabilities across multiple document formats with security-first access controls.
MCP server that provides a security gateway for AI agents, enforcing allow/confirm/deny policies on tool calls and requiring human approval for risky operations, with full audit logging.
An MCP server that downloads PDF files from URLs and converts them to Markdown format. Supports custom file names and directories, with security measures like SSRF protection.
Enables agents to inspect PDF, Microsoft Office, and Apple iWork documents without rendering them, providing page/slide counts, metadata, security signals, structure, and integrity as deterministic JSON via typed tools and batch operations.
Provides read/write file tools with lint validation to prevent agents from writing malformed Obsidian markdown, and constrains writes to a single configurable vault for security.
Percival Ubuntu is a security-focused MCP server for safe Ubuntu operations, optimized for the Nanobot agent. It enables file management and command execution under strict security policies.
A comprehensive Node.js server implementing Model Context Protocol (MCP) that enables filesystem operations, process management, and terminal session handling with an enterprise-grade security approach.
Read-only MCP server providing AI access to verifiable web, GitHub, and local sources, plus a managed fantasy entity catalog, with strong security and provenance tracking.
Enables AI assistants to interact with a local folder of Markdown notes, supporting listing, reading, searching, creating, and appending to notes with strict security boundaries.
GhostLink is a security-hardened MCP server that gives AI coding agents safe, deterministic access to local repositories through sandboxed, policy-gated tools for searching, reading, patching, and running curated commands.
Efficiently loads, caches, and delivers project file context for AI agents using the Model Context Protocol, reducing multiple file reads to a single call with LRU caching and security validation.
Enables local macOS automation via the Model Context Protocol, allowing file operations, system monitoring, shell commands, clipboard access, and screenshots with configurable security restrictions.