Search for:
Why this server?
Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.
Why this server?
An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool, implying RAG capabilities which depend on a vector database.
Why this server?
A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.
Why this server?
Aims to be portable, local, easy, and convenient to support semantic/graph-based retrieval of txtai "all in one" embeddings database, supporting vector search.
Why this server?
Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes, a form of vector retrieval.
Why this server?
A 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.
Why this server?
A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.
Why this server?
Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities.
Why this server?
This project is intended as an example of how to create a MCP server for Qdrant, a vector search engine.
Why this server?
A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.