"Arch Linux" matching MCP servers:

Serves an offline knowledge base — an aggregated corpus of reference works plus a RAG index over them and the user's own documents — to any local AI model over MCP. Enables offline assistants such as Open WebUI, OpenCode, or Jan to ground answers in searchable, provenance-tracked local content without any network connection.

Enables semantic search over Omarchy, Arch Linux, and Hyprland documentation, allowing users to query configuration and usage information via natural language in Cursor, OpenCode, or Claude Code.

Enables MCP clients such as Codex and Hermes Desktop to search files indexed by a local DigUp installation on macOS and Linux, returning matching passages and file paths.

Turn any folder or Git repo into safe, token-efficient context for LLMs, AI chats, and coding agents like Claude Code, Codex, and Cursor — local-first GUI, TUI, CLI, and a read-only MCP server with Live Context, agent journal, Smart Ignore, secret/PII redaction, Git scopes, and code compression. Windows, macOS, Linux.

Local-first MCP server providing semantic search over library docs, fully offline. Single Go binary speaks MCP over stdio against a vector index pinned to the binary version. Like Context7 with the internet turned off. Apache 2.0. Linux + macOS, also available as a container image.

Enables AI agents to query Linux kernel source code, git history, LKML discussions, Patchwork patches, and documentation with evidence-ranked results, citations, and explicit assumptions or contradictions.

Gives MCP clients a small remote Streamable HTTP tool surface for a Brain memory database, letting agents store facts, retrieve current-truth answers with evidence, delete memories, and check their effective identity. Each call is scoped by a customer identifier for per-customer isolation, and the endpoint can optionally be secured as an OAuth 2.1 resource server.

Exposes a support-ticket inbox as tools an AI client can use to list, inspect, triage, and update tickets, including LLM-based category, priority, and sentiment classification with a drafted first response informed by similar past tickets. It works with a mocked dataset styled after a home-loan partner-support inbox, with the tool interface designed so a real ticket source could be swapped in later.

An MCP server providing semantic memory storage and retrieval using vector embeddings powered by LanceDB and Google Gemini. It supports multi-tenant isolation and bucket-based organization for managing structured memories through natural language queries.

Enables coding agents to govern and access memory engines over MCP, with scoped read/write permissions, journaling, secret scrubbing, and bounded context delivery at session start.

Enables agents to full-text search internal markdown documents and list or create items on a Monday.com-style board, with write calls gated behind a preview-and-single-use-token flow, tool allowlists, and an audit log of every call. It also ships ready-made Gemini and Claude command-line agents that call these tools with human approval before any change.

Enables semantic search and question-answering over semiconductor component data stored in Excel files using ChromaDB vector database and HuggingFace language models through a RAG pipeline.

A Model Context Protocol server that bridges Microsoft Teams with MCP-compatible clients (LLMs, agentic frameworks, CLI), enabling chat integration, message search, and event streaming without traditional REST API endpoints.