"Connecting Supabase to Cursor for an AI-Powered Custom Greeting Card Website" matching MCP servers:

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

Enables searching through vectorized Cursor IDE chat history via a FastAPI service powered by LanceDB and Ollama.

Crawls and indexes documentation websites to Supabase with vector embeddings for RAG, using smart sitemap discovery and Jina AI for fast content extraction with multi-project support.

Enables semantic search over your Cursor IDE chat history by vectorizing prompts and storing them in LanceDB. Provides a Dockerized API to perform vector similarity searches against your chat history.

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

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

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

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

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.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

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.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

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

A local Retrieval-Augmented Generation system that enables AI agents to query and retrieve information from document collections using ChromaDB vector search and Ollama LLMs through a FastAPI interface.

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 server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.