"Design Architecture of Ontology-based AI-PHM Using Life Acceleration Algorithms" matching MCP servers:

Enables Claude Code to search and retrieve from a local knowledge base of markdown notes using hybrid semantic+keyword search, keeping data entirely offline.

Exposes document ingestion, retrieval (vector, vectorless, hybrid), and multi-turn chat tools for a LangGraph-powered RAG pipeline with streaming answers.

Enables AI assistants to semantically search your entire local conversation history from Claude Desktop, ChatGPT and Claude Code, and to retrieve, browse, ingest and report on those conversations. All embeddings run locally, so no cloud, API keys, or data leave your machine.

Provides AI-assisted long-term memory storage and retrieval with exact-phrase and semantic hybrid search, enabling save, read, delete, and admin recovery via MCP tools.

Exposes document retrieval as an MCP tool, enabling LLMs to search a local vector store of markdown documents. Includes a retrieval evaluation harness to measure hit rate and MRR.

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

MCP server to perform semantic and keyword searches across AI Nike-chan's public X posts and official website, with optional AI Gateway integration and vector index hosting on Vercel Blob.

Provides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

Exposes LangChain and Anthropic Claude capabilities as tools for generating production-ready RAG systems, Supabase vector stores, and document ingestion pipelines. It enables users to instantly scaffold AI infrastructure and document processing code through natural language prompts in MCP-compatible clients.

Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.

An MCP-compatible RAG backend using LangGraph and FastAPI, enabling chaining of AI model logic with document context search.

Munin 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 & Semantic

Enables Claude Desktop to search private documents using Azure AI Search and perform web searches with Bing, providing AI-enhanced results with source citations through Azure AI Agent Service or direct Azure AI Search integration.

A modular tool that combines RAG-based retrieval with Pinecone vector storage to create intelligent assistants capable of answering domain-specific questions from your knowledge base.

Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.

An MCP server for storing and retrieving memories using Qdrant vector search, acting as a semantic memory layer.

A local-first AI backend that combines Model Context Protocol, Retrieval-Augmented Generation, and an agent runtime to answer questions from a private knowledge base.

Official Kimss MCP server — call your Kimss agents, models, files, and vector stores from Cursor and other MCP clients using your API key.