"Home Assistant integration with Codex or ChatGPT" 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.

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.

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.

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

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

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 conversational querying across a personal research library of PDFs, DOCX, and other documents using a vector database. It provides tools for document summarization, finding related papers, and high-accuracy retrieval for AI clients like Claude Desktop.

MCP server that provides secure read-only access to a local folder, enabling file listing, reading, semantic search (RAG), and indexing status via natural language, integrated with Claude Desktop and a custom agent loop.

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.

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.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

Enables AI-powered semantic search and question-answering for LiveKit documentation using Pinecone vector search and real-time web search with Tavily, providing detailed responses with source attribution.

This server enables semantic search capabilities using Qdrant vector database and OpenAI embeddings, allowing users to query collections, list available collections, and view collection information.

Enables hybrid search (BM25 + semantic with RRF) over documentation, with Thai language support. It exposes MCP tools for adding documents and searching via Qdrant and Ollama/OpenAI, using a stateless Streamable HTTP transport.

Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.

Enables storing and retrieving text in a local vector store using Qdrant, running fully locally with no external database or API key.

Provides comprehensive management of OpenAI Vector Stores, allowing AI assistants to upload files, manage vector databases, and handle batch operations via the OpenAI API. It supports multiple deployment methods, including Cloudflare Workers and local NPM installation, for seamless integration with MCP-compatible clients.

Enables querying medical documents stored in Qdrant using semantic retrieval, allowing users to search PubMed articles through the Model Context Protocol.

Enables retrieval-augmented question answering over LangGraph documentation, allowing MCP-compatible hosts to query a semantic vector store and receive context-aware responses with source attribution.