"Google Assistant" matching MCP servers:

Pinecone Assistant MCP server

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

Enables AI assistants to save, retrieve, and manage research content using ChromaDB vector storage with semantic search, topic organization, and automatic deduplication powered by OpenAI embeddings.

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.

A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.

A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.

Enables semantic and keyword search across locally indexed PDF shelves—device manuals, meeting minutes, and scientific papers—with date, device, and author filters, returning structured citations and neighbouring passage context for a speech assistant. It also exposes shelf and document metadata, plus a reindex signal, over streamable HTTP backed by Postgres with pgvector.

Enables AI-powered medical information retrieval through FHIR clinical document search and GraphRAG-based exploration of medical entities and relationships. Combines vector search with knowledge graph queries for comprehensive healthcare data analysis.

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

Enables AI to save, organize, search, and synthesize research materials using a local vector database with support for both OpenAI and Ollama backends.

Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.

Local podcast transcription, embedding, and semantic search with an MCP server for AI assistant integration. All processing runs on your machine.

A persistent long-term memory system that enables AI clients to store and recall notes, code, and research via semantic search. It utilizes Google Gemini embeddings and Supabase pgvector to provide a secure, searchable 'Second Brain' for MCP-compatible applications.

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 question-answering over uploaded documents using vector embeddings and Google AI. Supports document organization with tags, section-aware queries, and hierarchical markdown structure preservation.

An MCP server that gives Claude (and any MCP client) a shared team knowledge base with semantic search. Backed by ChromaDB and Google Gemini embeddings — works across languages (RU + EN).

RAG-enabled MCP server that uses Google Gemini for embeddings and Supabase for vector storage, enabling semantic search and document similarity matching through natural language queries.