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Real Estate MCP Server

A demo showcase of a comprehensive MCP Server setup using a real estate websites and companies data. This MCP has been incorporated into several other related real estate (renting, buying, consulting) projects. Reach out to me personally if you work on similar projects or would like to just have a bainstorm session!

Installation
Add the following to your MCP client configuration file.

Configuration

View docs
{
  "mcpServers": {
    "agentic-ops-real-estate-mcp": {
      "command": "C:/absolute/path/to/real-estate-mcp/.venv/Scripts/python.exe",
      "args": [
        "C:/absolute/path/to/real-estate-mcp/main.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

You run a Real Estate MCP Server that centralizes property listings, agents, markets, clients, and area intelligence. It exposes a flexible set of tools, resources, and prompts you can use from an MCP client to analyze data, match properties to clients, and track market trends in real time.

How to use

Connect to your MCP server using a client that supports the MCP protocol. You can operate in STDIO mode for local usage or in SSE mode for remote or web-based access. In STDIO mode, you interact through the client and the server processes on the same machine, delivering ultra-fast responses. In SSE mode, the server streams updates to connected web clients and supports multiple simultaneous connections.

How to install

Prerequisites: you need Python installed on your system, a command shell, and a working internet connection to fetch dependencies. You also need a shell or terminal to run commands.

Step-by-step setup:

1) Clone the project repository

git clone https://github.com/agentic-ops/real-estate-mcp.git
cd real-estate-mcp

2) Create and activate a Python virtual environment

python -m venv .venv

# On Windows:
.venv\Scripts\activate

# On macOS/Linux:
source .venv/bin/activate

3) Install dependencies

pip install -r requirements.txt

4) Install the MCP entry point so you can run it via your MCP client

mcp install main.py

5) Run the server locally in STDIO mode

python main.py

6) Run the server in SSE mode for remote access

python main.py sse

Configuration and running notes

If you use Claude Desktop or a similar client, you may configure the server to run from your local environment using your preferred Python executable. The following Windows example shows how the Python interpreter from your virtual environment can be wired to the main entry point.

{
  "mcpServers": {
    "real-estate": {
      "command": "C:/absolute/path/to/real-estate-mcp/.venv/Scripts/python.exe",
      "args": ["C:/absolute/path/to/real-estate-mcp/main.py"],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

Notes on transport modes and endpoints

STDIO transport is recommended for local usage and Claude Desktop, delivering the fastest responses with minimal setup. SSE transport is available for remote access and multi-client scenarios, providing real-time streaming updates.

STDIO usage example: run the server with python main.py. SSE usage example: run python main.py sse and connect clients to the SSE endpoint at the host and port you configure.

Examples of available actions from the server

You can search and filter properties, analyze market trends, manage agent dashboards, and match clients to properties. For instance, you can retrieve area-based property listings, look up agent performance dashboards, read market overviews, and generate client property matches.

Available tools

search_properties

Search properties by area, price, features, and other criteria to return matching listings and insights.

filter_properties

Filter property listings by defined criteria such as price range, bedrooms, property type, and location.

read_market_overview

Retrieve current market trends, price movements, and overall market health for quick assessment.

match_client_preferences

Match client preferences to available properties and generate prioritized property recommendations.

read_agent_dashboard

Fetch agent performance metrics and portfolio dashboards for performance review.

read_property_insights

Provide detailed insights on a specific property, including comparable data and market context.