RealEstate MCP

Overview

The RealEstate MCP server transforms the way Australian property data is accessed by AI assistants. Traditional real‑estate portals offer a limited set of rigid, suburb‑centric filters that rarely align with how buyers actually think about homes. A user might ask for “renovated 3‑bedroom houses in the Box Hill High School zone,” a query that conventional interfaces cannot satisfy. By leveraging large language models and the Model Context Protocol, this server enables natural‑language property search that understands nuanced user intent and maps it to real‑estate data.

At its core, the server exposes a suite of search tools that an AI assistant can invoke. These tools perform semantic searches against a vector database populated with property details and auction results. When a user poses a question, the assistant forwards it to the MCP server; the server then queries the vector store, retrieves the most relevant listings, and returns a concise answer. This workflow allows developers to embed advanced property search directly into chatbots, recommendation engines, or virtual agents without building custom query logic.

Key capabilities include:

• Semantic indexing of property listings and auction data, enabling searches by description, location, or school zone rather than just raw metadata.
• Natural‑language query handling that interprets user intent and translates it into vector similarity queries.
• FastAPI‑based MCP server that offers a standard interface for tool invocation, making it easy to integrate with existing AI pipelines.
• Demo client demonstrating end‑to‑end usage, from ingesting data to querying the model.

Real‑world scenarios where this MCP shines are plentiful. Real‑estate agents can provide chat assistants that answer complex queries about available homes in specific school districts or with particular renovation features. Home‑buyers can use conversational agents to discover listings that match their lifestyle preferences, while data analysts can extract insights about market trends through natural‑language queries. Additionally, the modular ingestion pipeline allows teams to keep the vector database fresh with up‑to‑date auction results, ensuring that recommendations reflect current market conditions.

Because the server is built on MCP, it fits neatly into modern AI workflows: developers can expose it as a tool to Claude or other assistants, enabling seamless, context‑aware property search without exposing raw APIs. The combination of semantic indexing, natural‑language understanding, and MCP integration gives this server a distinct advantage over traditional filter‑based search engines.