RE Data Refinery MCP Server
Provides real estate property intelligence and investment scoring based on live Zillow data, including property listings, price history, tax assessments, school ratings, and natural-language search.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@RE Data Refinery MCP ServerWhat are the top flip deals in Columbus under $200k?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
RE Data Refinery MCP Server
A Model Context Protocol (MCP) server that turns messy real estate data into clean, scored, AI-ready property intelligence for Columbus, OH and surrounding metro cities.
Unlike generic property APIs, the RE Data Refinery combines live Zillow data with county-level enrichment (GIS, tax delinquency, sheriff sales, permits, probate records) and computes proprietary investment scores for every listing. Agents pay only for the queries they run — no subscriptions, no API tiers — via the x402 micropayment protocol on Base mainnet.
Live data source: ZillAPI
Coverage: 150+ properties seeded across 14 Columbus metro cities
Payment: USDC on Base, $0.35 per paid lookup
Transport: stdio / SSE for Claude Desktop, Claude Code, ChatGPT Desktop, Hermes, and other MCP clients
What makes it different
Feature | RE Data Refinery | BatchData / USDV Capital |
Pricing | Pay per query via x402 (no subscription) | Free tiers or monthly subscriptions |
Scoring | Flip, wholesale, rental yield, market heat | Usually absent or generic |
Enrichment | County GIS, tax delinquency, sheriff sales, permits, probate | Typically surface-level listing data |
On-chain settlement | USDC on Base via Permit2 + CDP facilitator | Not applicable |
Tools (10)
Tool | Description | Price |
| API health, cached property count, rate limit status | Free |
| Upstream ZillAPI credit balance | Free |
| List scored properties by city with optional price filters | $0.35 (live), free (cached) |
| Full scored detail for a single property by ZPID | $0.35 |
| Price/transaction timeline for a property | $0.25 |
| Tax and assessment history for a property | $0.25 |
| School ratings near a property | $0.25 |
| Natural-language property search | $0.50 |
| Search filtered/scored by investment criteria | $0.50 |
| Show x402 configuration and active API base URL | Free |
Quick start
1. Install dependencies
pip install "x402>=2.20.0" eth-account httpx2. Clone the repository
git clone https://github.com/areshms/re-refinery-mcp.git
cd re-refinery-mcp3. Configure your environment
Create a .env file in the project root:
# Required for paid Worker lookups
EVM_PRIVATE_KEY=0x...
# Optional
X402_SPEND_CAP=$1 # max per-payment USD cap (default: $1)
REFINERY_BASE_URL=https://re-data-refinery.ares-hms.workers.dev
REFINERY_LOCAL_URL=http://localhost:5004Your wallet must hold USDC on Base to pay for lookups.
4. Add to your MCP client
Claude Desktop / Claude Code:
{
"mcpServers": {
"re_refinery_mcp": {
"command": "python3",
"args": ["/path/to/re-refinery-mcp/re_refinery_mcp.py"],
"env": {
"EVM_PRIVATE_KEY": "${EVM_PRIVATE_KEY}",
"X402_SPEND_CAP": "$1"
}
}
}
}Local development mode
To develop or test without spending USDC, disable x402 and point at a free local API:
export REFINERY_ENABLE_X402=false
export REFINERY_LOCAL_URL=http://localhost:5004
python3 re_refinery_mcp.py --transport stdioPaid endpoints then call localhost:5004 instead of the Worker.
How payments work
Your agent requests a paid endpoint.
The Worker responds with HTTP 402 Payment Required and an x402 payment requirement.
The MCP client signs a Permit2 USDC transaction on Base.
The CDP facilitator verifies the signature and settles the payment on-chain.
The Worker returns the requested data.
All of this is handled automatically once EVM_PRIVATE_KEY is configured.
Scoring methodology
Every property is enriched with:
flip_score (0–100): discount vs. Zestimate, days on market, neighborhood median, age, lot size
wholesale_score (0–100): equity spread, tax delinquency, DOM, owner-occupancy, price vs. Zestimate
rental_yield_pct: annual rent estimate ÷ price
market_heat: Hot / Warm / Cool based on days on market and recent price trend
Scores are recomputed against neighborhood medians drawn from the live-refinery cache.
Requirements
Python 3.11+
x402>=2.20.0eth-accounthttpxA wallet with USDC on Base mainnet
Worker
The Cloudflare Worker backing this server is deployed at:
https://re-data-refinery.ares-hms.workers.devIt is the first real estate data refinery using Cloudflare's x402 monetization gateway.
License
MIT
Built by Hightower Marketing — making real estate data work for AI agents.
This server cannot be installed
Maintenance
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