"Gathering Context from an Ubuntu Server" 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.

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.

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

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.

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

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 clients to perform sandboxed filesystem, directory, Ubuntu system, network, git, and code/text operations through 38 typed tools confined to a workspace. Ships with a browser-based web console that provides an interactive Linux terminal, a live tool playground, and real-time CPU, memory, and disk monitoring.

A secure protocol server that allows AI assistants to safely interact with Ubuntu systems through controlled file operations, command execution, package management, and system information retrieval.

An MCP server that connects AI assistants to YNAB budgets, enabling natural language queries about finances backed by full API coverage and built-in YNAB methodology knowledge.

A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

Durable project-memory MCP: decisions, constraints, and pipelines across Claude sessions

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

Enables AI assistants to search and retrieve encrypted local screen history, including OCR text, recent activity, context around moments, daily rollups, and screen snapshots.

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

A Model Context Protocol server that gives AI assistants persistent, scoped memory by saving and retrieving short notes (mementos) across sessions and projects, enabling dynamic learning and context injection without heavy dependencies.