A
license-
qualityB
maintenanceMCP 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.
3
MIT
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"A system for managing emails, calendar events, and summarizing Obsidian notes" matching MCP servers:
- Flicense-qualityDmaintenanceProvides semantic search over Obsidian markdown notes using BAAI/bge-m3 embeddings and ChromaDB, accessible via the Model Context Protocol.
- FlicenseBqualityDmaintenanceA Model Context Protocol server that enables semantic vector search over Obsidian notes, allowing AI assistants to intelligently search and retrieve relevant note content.12
- Alicense-qualityCmaintenanceA Model Context Protocol server that enables semantic search and retrieval of Apple Notes content, allowing AI assistants to access, search, and create notes using on-device embeddings.2,6732MIT
- Flicense-qualityFmaintenanceEnables semantic search across Obsidian vaults using vector embeddings and ChromaDB. Supports multiple vaults with real-time indexing and provides both MCP server and CLI interfaces for natural language querying of notes.4
- Alicense-qualityDmaintenanceA 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.MIT
- Flicense-quality-maintenanceEnables semantic search and content retrieval from local Obsidian vaults through the Model Context Protocol. It allows AI assistants to query notes by meaning, filter by tags, and access full note content for enhanced knowledge integration.
- AlicenseBqualityBmaintenanceA flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.389100MIT
- Alicense-qualityDmaintenanceA vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.2MIT
- Alicense-qualityDmaintenanceEnables persistent memory and semantic search for development workflows with hierarchical compression. Store and retrieve development knowledge across IDE sessions using natural language queries, circumventing context window limitations.2AGPL 3.0
- Flicense-qualityDmaintenanceA Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.
- Flicense-qualityBmaintenanceEnables semantic search over personal markdown notes by indexing them into a vector database and exposing search, reindex, and status tools via MCP.
- Alicense-qualityDmaintenanceA Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.Apache 2.0
- Alicense-qualityCmaintenanceAn integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.240Apache 2.0
- Flicense-qualityDmaintenanceAn MCP-compatible RAG backend using LangGraph and FastAPI, enabling chaining of AI model logic with document context search.1
- Flicense-qualityDmaintenanceAn MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.5
- Flicense-qualityCmaintenanceEnables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.
- Flicense-qualityBmaintenanceMunin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic3
- Flicense-qualityDmaintenanceEnables semantic search and natural language product recommendations using ChromaDB vector store and Azure OpenAI embeddings. Supports multi-filter search by category, brand, and price with 8 specialized MCP methods for intelligent product discovery.
- Flicense-quality-maintenanceA local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.