Real Estate MCP Server
Property search and market analysis from Redfin with neighborhood insights
Documentation
Real Estate MCP — Property Search & Details for AI Agents (nexgendata/real-estate-mcp-server) Actor
MCP server exposing US property search and full listing detail (Redfin-sourced) as agent tools. Connect Claude, Cursor, n8n or the OpenAI Agents SDK.
- URL: https://apify.com/nexgendata/real-estate-mcp-server.md
- Developed by: NexGenData (community)
- Categories: AI, Travel, MCP servers
- Stats: 43 total users, 9 monthly users, 100.0% runs succeeded, 0 bookmarks
- User rating: No ratings yet
Pricing
from $50.00 / 1,000 property records
This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A".
How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official JavaScript/TypeScript client:
npm install apify-client
In Python projects, use official Python client library:
pip install apify-client
In shell scripts, use Apify CLI:
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## Real Estate MCP
A Model Context Protocol server that gives AI agents US residential real-estate data — property search and full listing detail, sourced from Redfin — as callable tools. For real-estate research, valuation and lead agents.
### 🛠 Tools (2)
- `get_property_details` — Full detail for a specific property.
- `search_redfin_properties` — Search Redfin property listings by location and filters.
### 🔌 Connect (Claude Desktop / Cursor / n8n / OpenAI Agents SDK)
Add this MCP server to your client config:
```json
{
"mcpServers": {
"real-estate": {
"url": "https://nexgendata--real-estate-mcp-server.apify.actor/mcp"
}
}
}
Sample agent prompt:
Search for 3-bed homes under $600k in Denver and pull full details on the top result.
Pricing: $0.05 per property record (Pay-Per-Event). Runs in Standby mode.
Related NexGenData actors
| Use case | Actor |
|---|---|
| Redfin listings, sold history, comps in flat JSON | Redfin Real Estate Scraper |
| Redfin listings via MCP (agent tools) | redfin-mcp-server |
| UK property listings from Rightmove | Rightmove UK Real Estate |
| Singapore HDB resale transactions | SG HDB Resale Prices |
| Singapore URA private-property transactions | SG URA Property Transactions |
| Denmark housing market via Boliga | Boliga Denmark Real Estate |
| AI-native financial data for LLM agents | Finance MCP Server |
| Local-business + map data MCP for agents | Google Maps MCP Server |
| Multi-source travel & rental MCP | Travel MCP Server |
Home: thenextgennexus.com Full catalog: apify.com/nexgendata
Actor input Schema
debug (type: boolean):
Enable verbose logging in the MCP server stdout. Helpful when wiring a new AI client (Claude Desktop, Cursor, n8n) and you want to see every tool invocation. Leave off in production.
maxToolCallSeconds (type: integer):
Maximum time a single MCP tool invocation may run before the server aborts it and returns a timeout error to the AI client. Increase only if your agent issues unusually long-running property scrapes (e.g. fetching hundreds of comps in one shot).
Actor input object example
{
"debug": false,
"maxToolCallSeconds": 90
}
API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
JavaScript example
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
token: '<YOUR_API_TOKEN>',
});
// Prepare Actor input
const input = {
"debug": false,
"maxToolCallSeconds": 90
};
// Run the Actor and wait for it to finish
const run = await client.actor("nexgendata/real-estate-mcp-server").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
Python example
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")
# Prepare the Actor input
run_input = {
"debug": False,
"maxToolCallSeconds": 90,
}
# Run the Actor and wait for it to finish
run = client.actor("nexgendata/real-estate-mcp-server").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
CLI example
echo '{
"debug": false,
"maxToolCallSeconds": 90
}' |
apify call nexgendata/real-estate-mcp-server --silent --output-dataset
MCP server setup
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=nexgendata/real-estate-mcp-server",
"--header",
"Authorization: Bearer <YOUR_API_TOKEN>"
]
}
}
}
OpenAPI specification
{
"openapi": "3.0.1",
"info": {
"title": "Real Estate MCP — Property Search & Details for AI Agents",
"description": "MCP server exposing US property search and full listing detail (Redfin-sourced) as agent tools. Connect Claude, Cursor, n8n or the OpenAI Agents SDK.",
"version": "0.0",
"x-build-id": "aIvztspAf1kqgsI18"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/nexgendata~real-estate-mcp-server/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-nexgendata-real-estate-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/nexgendata~real-estate-mcp-server/runs": {
"post": {
"operationId": "runs-sync-nexgendata-real-estate-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/nexgendata~real-estate-mcp-server/run-sync": {
"post": {
"operationId": "run-sync-nexgendata-real-estate-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"debug": {
"title": "Debug logging",
"type": "boolean",
"description": "Enable verbose logging in the MCP server stdout. Helpful when wiring a new AI client (Claude Desktop, Cursor, n8n) and you want to see every tool invocation. Leave off in production.",
"default": false
},
"maxToolCallSeconds": {
"title": "Per-tool timeout (seconds)",
"minimum": 5,
"maximum": 600,
"type": "integer",
"description": "Maximum time a single MCP tool invocation may run before the server aborts it and returns a timeout error to the AI client. Increase only if your agent issues unusually long-running property scrapes (e.g. fetching hundreds of comps in one shot).",
"default": 90
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}