"Diagnosing Issues with My Remote Ubuntu Server Configuration" matching MCP servers:

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.

MCP servers for querying and submitting Ubuntu data via ubq, covering bugs, packages, versions, and merge requests with multiple authentication providers.

Enables AI assistants to safely interact with an Ubuntu system through policy-controlled file access, command execution, package search, and system monitoring, with full auditing and defense-in-depth security.

Enables AI assistants to interact with Ubuntu VMs via SSH, including executing commands, simulating keyboard/mouse input, capturing screenshots, and controlling GUI applications remotely.

Enables triage of GitHub issues by fetching and filtering issues, checking claim status, and listing new open issues across a watch-list.

Enables comprehensive Linux desktop agent capabilities for Ubuntu/Debian systems, including system state monitoring, package management, persistent terminal sessions, and desktop control via mouse, keyboard, and screen capture, with support for multiple remote machines and a built-in permission system.

Enables AI assistants to list, search, and inspect issues on any public GitHub repository via natural language.

Lets agents read a GitHub repository's live issues through read-only tools for listing, filtering, fetching details with comments and cross-references, searching, and exploring labels and milestones, with transparent pagination and provenance notes.

Enables AI assistants to control Ubuntu desktops through screenshots, mouse clicks, and keyboard interactions using AT-SPI integration and computer vision. It features optimized element detection and workflow batching for fast and accurate visual interaction with desktop applications.

Provides a read-only summary of Ubuntu server status including system version, resource usage, apt updates, service health, security logs, and hardware information to AI clients via MCP tools.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

Enables AI assistants to discover, filter, and triage GitHub issues across repositories, with tools for fetching issue details, listing issues by state/labels, and finding related pull requests.

Exposes GitHub issue operations as tools for LLM agents, enabling listing, creating, commenting on, closing, and searching issues via natural language.

Trust layer for 412 official Malaysian public datasets, with freshness, licences, and a public read-only MCP server. No API key.

A template MCP server that provides a basic structure and configuration for building Model Context Protocol servers. Includes Docker support, environment configuration, and examples for both STDIO and SSE transport modes.

Enables LLM agents to explore and search a 1C:Enterprise configuration codebase, including metadata objects, module procedures, call graphs, and semantic similarity search.

Turns local Codex session transcripts into a searchable memory that MCP-compatible coding agents can query to retrieve relevant past sessions or exact commands and fixes.

A production-ready template for developing Model Context Protocol (MCP) servers using Python and FastMCP, with example tools like a multiply calculator, code review prompt generator, and Red Hat logo tool.

Enables developers to build, run locally with debugging, and deploy to the cloud custom remote MCP endpoints hosted on Azure Functions, with built-in Microsoft Entra authentication. Sample projects demonstrate snippet CRUD and QR code tools, resource templates, prompt workflows, and an interactive weather demo UI.

Enables agents to search a local, fully offline personal document library (Markdown/txt/PDF/docx) via SQLite FTS5 trigram search with subject-terminology expansion, returning every hit with its domain, document title, section path, and page or offset provenance. It also exposes domain statistics, incremental ingestion, and document deprecation as tools, alongside a read-only, runtime-persona-scoped ask/coverage mode.