"Gathering Context from an Ubuntu Server" matching MCP servers:

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

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.

A fully working MCP server built from scratch in plain Node.js, implementing tools, resources, prompts, notifications, and sampling according to the MCP specification, designed to connect to Claude Desktop or any MCP client.

Enables users to expose arbitrary HTTP APIs as configurable MCP tools by defining tool specifications in a JSON configuration file.

Enables clients to interact with a bundled documentation corpus through MCP tools and resources over HTTP or stdio, and provides a trajectory evaluation harness for grading agent performance.

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.

Local-first documentation for AI agents

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Deterministic dependency + CVE context for AI coding tools, over the Model Context Protocol. A ~0.85 MB pure-Rust MCP server.

An MCP server that reads all your Claude Code project memory files and exposes them as tools. Lets any Claude instance — in any project, or via Claude.ai — query your full project history and preferences.

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Aggregator MCP proxy that collapses N downstream MCP servers into 4 meta-tools with progressive tool discovery, and compresses large tool outputs (HTML→Markdown, JSON summarization) with full-output retrieval via read_more and a per-session token-savings report.

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

Intelligent context manager for AI coding assistants that uses a three-level memory system (core, active, archive) to remember project context across conversations.

A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.

An MCP server that acts as a context-aware code retrieval broker for AI coding agents, enforcing backpressure and token budgeting to prevent context window overflow.

Provides local, explicitly scoped memory for coding agents via MCP, storing durable project knowledge in a per-repository SQLite database with tools to record, search, and retrieve context.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.