"Information or Uses for a Rag" matching MCP servers:

A live MCP server that connects AI assistants to a running Obsidian instance, enabling full app automation including reading notes, clicking UI elements, managing plugins, and executing JavaScript.

A native Nextcloud app that exposes the instance as an MCP server so Claude and other MCP clients can read, search, and (with admin-granted permissions) edit files, notes, calendars, Deck cards, and Talk conversations. It runs inside Nextcloud with OAuth or app-password sign-in, applying each user's own Nextcloud permissions and admin-controlled per-operation grants.

Enables sandbox-less frontends such as mobile apps to reach the WeRead official gateway over Streamable HTTP by supplying only a URL and an access token, with no local process required. Exposes 14 read-only tools covering search, shelf, notebooks, reading progress and statistics, highlights, public reviews, and recommendations, plus a token-protected dashboard for shelf, notes, and shared-reading annotations.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

A robust MCP server that transforms OneNote notebooks into an AI-accessible knowledge base for Gemini Spark, enabling natural language queries to list, read, and search notes via Microsoft Graph API.

Persists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.

Enables AI agents to create and interact with ASCII-based Zen gardens through tools for raking sand, placing stones, and practicing digital meditation. It provides resources for random Zen koans and generates meditation guides based on the current garden state.

MCP server for Obsidian vaults, optimised for Cursor agents — safe note editing, lean tool surface, direct filesystem access.

MCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.

Model Context Protocol (MCP) server for Joplin note-taking app, enabling AI to read, search, and modify notes via sandboxed scripts.

MCP server for a self-hostable personal planner — exposes memo, todo, calendar event, and cross-resource full-text search tools so Claude, Cursor, or any MCP client can manage your schedule and notes in natural language.

Enables MCP clients to search and read notes from an Obsidian or markdown vault through four governed tools, with enforced read-only access, a page budget, and path sandboxing.

MCP Server for Bear note taking app available as Claude Desktop extension or standalone server for any other AI tool

Provides read-only access to finished meeting transcripts for AI assistants like Claude Code or Codex, enabling them to answer questions or draft summaries based on the transcriptions.

An MCP server that lets Claude or ChatGPT read, create, and edit mind maps stored in a GitHub repository, with support for local and HTTP transport.

Enables AI assistants to safely read, search, create, append, update, and delete Apple Notes on macOS, with every write or delete requiring human confirmation and operations limited to a single note at a time.

Enables Claude to control the AI Coach desktop pet, making the mascot react to the conversation, accept care, show messages, act out activities, preview local files, and manage user memos.

Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.

A local MCP server that maintains a persona profile (contact library) for AI assistants, allowing them to create, search, retrieve, supplement, and correct information about people mentioned in conversations via tools like search_profiles, get_profile, create_profile, update_profile, add_facts, update_fact, and delete_fact.

Builds a dynamic knowledge graph from your notes and uses LLM reasoning to discover blindspots, hidden cross-domain connections, track concept evolution, and spark creative inspiration.