"Extracting and Processing Real Estate Listings Using Vector Databases" matching MCP servers:

Enables LLMs and agents to interact with relational databases (SQL Server, MySQL, PostgreSQL) through MCP tools. Supports executing queries, inserting records, listing tables, and exposing database schemas with secure credential management.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

A shared, persistent MCP memory server for coding agents that enables storing and retrieving project decisions and context across different tools like Claude Code, Codex, and Cursor using semantic vector search.

Integrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

Provides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.

Enables AI assistants to save and recall information from files or free-form notes using natural language, acting as a long-term memory system.

Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.

An MCP server that connects to a SQLite database of Zillow real estate data, enabling users to explore property values, rent indexes, and forecasts through a chat interface to make informed investment decisions.

Provides AI coding agents with persistent, long-term memory through local semantic search and SQLite storage. It enables agents to save and retrieve architectural decisions or project context across different conversation sessions without requiring cloud services.

Enables AI assistants to securely search and retrieve information from a user's encrypted Joplin notebook via vector memory and MCP.

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.

Processes emails from Outlook with date filtering, storing them in SQLite databases while generating vector embeddings for semantic search capabilities in MongoDB.

An MCP (Multi-Agent Conversation Protocol) Server that provides access to Google's Real-Time Bidding API, enabling programmatic interactions with RTB functionalities through natural language.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Generates production-ready short video scripts from real-time web search data using Tavily and Gemini, exposed as MCP tools.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.

A semantic proxy that reduces AI agent token usage by exposing only three core tools and using local vector embeddings to search for and execute hundreds of underlying MCP tools. It streamlines communication between agents and MCP Routers by identifying relevant tools through natural language queries.

Enables document search and retrieval using TF-IDF vector similarity across HTML and PDF files. Provides ingest, query, and vector store management capabilities through both HTTP API and MCP stdio interfaces.

Enables semantic search and document retrieval from OpenAI Vector Store, allowing users to search documents using natural language queries and fetch complete document contents through ChatGPT.