"How to operate notes in the Memos app" matching MCP servers:

MCP server that enables AI assistants to interact with the MemOS API, providing memory search, feedback management, and knowledge base operations via MCP-compliant tools.

A personal knowledge management system that transforms daily notes into organized, searchable knowledge using the Model Context Protocol.

Provides SQL-backed semantic search over indexed notes using pgvector, exposing tools to search and list note sources via natural language.

Enables AI assistants to search and edit Apple Notes on macOS using semantic search, with safe write operations and full local indexing.

Provides semantic search and knowledge base management for the micro-app micro-frontend framework by ingesting its source code and documentation.

A Python MCP server for note management with RAG capabilities, enabling adding, searching, asking questions, summarizing, and deleting notes, using PostgreSQL and ChromaDB with Google Gemini for embeddings and generation.

MCP server that indexes your Obsidian notes into a Milvus vector database and enables querying them via a local or OpenAI LLM, with real-time synchronization.

Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.

Turns markdown note directories into an MCP knowledge assistant server, enabling hybrid search, explanations, quizzes, and comparisons for any MCP-compatible AI tool.

Enables semantic search and retrieval over an Obsidian vault using local or API-based embeddings, allowing AI assistants to find notes by meaning, get related content, and pull context during conversations.

Enables AI assistants like Claude to search and reference your Apple Notes using semantic search and RAG capabilities, with fully local execution and no API keys required.

Enables managing and searching markdown notes with semantic search, question answering, and note generation, and provides an MCP server for GitHub Copilot integration.

MCP server for processing raw book notes into structured, searchable knowledge using embeddings and semantic search.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A local MCP server that enables full-text and semantic search over your own Telegram chats using your personal MTProto login, with everything running locally.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables querying, listing, and summarizing personal knowledge base documents using RAG with hybrid search and LLM.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

A Model Context Protocol server that enables semantic search and RAG over your Apple Notes, allowing AI assistants like Claude to search and reference your notes during conversations.