Property Intelligence Real Estate MCP Server Case Study

Data Intelligence

Property Risk Data, Scattered Across Government and Commercial Sources

PropertyScoop is a PropTech data startup building a property intelligence product for buyers, agents, lenders, and insurers who need fast, address-level environmental and infrastructure risk data—drinking-water compliance, RF exposure, noise, tree-strike risk, substation proximity, sun orientation. That data exists, but it's scattered across separate government and commercial sources, each with its own format and gaps. Getting it wrong on safety categories like drinking-water compliance undermines product trust. TechAvidus built the standalone MCP server that aggregates this data and exposes it to AI agents securely.

Data Reliability & Architecture

The Real Challenge: Data Reliability & Architecture

While developing the system, we had to address critical business and engineering hurdles:

Business Challenge: As an early-stage startup, PropertyScoop's product is the data. Inconsistent or wrong information—especially for drinking-water compliance—would directly undermine customer trust. There was no room to redo it later.

Latency & Partial Failures: Reconciling response times across independent external data sources (EPA, FCC, satellite ML, noise models) queried per single address request.

Metered Billing & Entitlement: Designing per-tool, per-call metered billing with individual free-tier counters and graceful "tool not enabled" responses enforced at the gateway layer.

Data Normalization: Normalizing inconsistent government records (e.g., private-well addresses lacking EPA data) into a consistent schema.

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"They translated a complex integration challenge into a practical, maintainable solution that aligned with our product vision."

Toby Ehrenkranz · PropertyScoop

Solution Architecture

A Standalone MCP Server Built for AI Agents, in Weeks

TechAvidus designed and built a standalone remote MCP server exposing independent property-intelligence tools behind one normalized JSON-RPC 2.0 interface, over streamable-HTTP transport. The build included API-key authentication, feature-level per-tool entitlement, and per-tool metered billing with individual free-tier caps.

The project was delivered in weeks from initial development through production deployment, keeping the scope focused on core product requirements like point-in-time snapshot APIs.

Architecture & Capabilities

Property Intelligence Capabilities & Architecture

The server acts as an orchestration and aggregation layer, routing requests across government and third-party geospatial datasets and returning results through a consistent interface with isolated fallback handling.

Key Feature Modules:

Water Quality: Pulls EPA SDWIS compliance records, compliance grades (A-F), violation counts, and gracefully handles private-well exceptions with an explicit "no data" state.

RF/EMF Exposure & Noise Scoring: Estimates radiofrequency exposure from FCC transmitters within a 2-mile radius and combines transportation noise metrics against EPA 65 dB loud thresholds.

Falling-Tree Risk & Substation Proximity: Blends tree density, historical wind speed, and precipitation analytics alongside geospatial proximity mapping for nearby electrical infrastructure.

Facing-Direction Detection: Uses satellite imagery and a third-party ML classification model to calculate cardinal property orientations (labeled experimental).

System Overview

Technology Stack

Language / Runtime

Node.js, TypeScript

Protocol & SDK

MCP, JSON-RPC 2.0

Streamable HTTP, MCP SDK

Authentication

API-key (`X-API-Key`)

Feature-level entitlement

External Integrations

EPA, FCC, Satellite-ML, Noise

Storm, Weather, Substation data

Client Compatibility

Claude Desktop, Cursor, Windsurf

Via mcp-remote bridge

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Need Property Data or Risk Feeds Integrated into AI Agents?

We help PropTech and location intelligence companies expose data and workflows to AI agents through custom, secure, and production-ready Model Context Protocol servers with robust metering and fallback controls.

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What Our Clients Are Saying

I worked with TechAvidus for over 2 years. The team is incredible. They are very responsive, responsible, dedicated, and capable. I strongly recommend the team for any project, however complex.

Alexander Z Stanley

Business Development Manager

Keshu, Bhavesh and their team are outstanding. Not only were they able to build a very sophisticated and customized portal for our thousands of customers, but they did so with skill, efficiency, professionalism, and offered wonderful suggestions and recommendations along they way. Even more impressive, they were able to do all of this in spite of the convoluted direction we provided them with.

We've since expanded our relationship with them to cover many additional web development jobs. Choosing to work with TechAvidus was one of the best decisions we've made in recent years. We recommend them unconditionally.

Rob Price

President, Gatekeeper Press

"The vendor did an outstanding job. They paid attention to every detail of project scope and made prompt corrections when needed. I would definitely work with them again."

Jason Lorenti

Digital Development Manager at Evolved Office LLC

Love to work with the team with a fair pricing and very valueable developers for komplex usecase. Actually we worked on a really big module based application and every work is awesome done.

Matthias Grdan

CEO | Digital Transformation

They were very responsive and the quality of the work was good. We experienced a few challenges with the back and forth during deliveries and some unexpected delays but they were always receptive to feedback and responded well to our concerns. I'd happily recommend them, especially Megha as the Project Manager and Savan as the Developer.

Marcos Queiroz

Director at Rapido

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Digital Marketing

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Mobile

E-commerce & CMS

Company

Community

Turning Scattered Property-Risk Feeds Into
One AI-Ready Server for PropertyScoop

Our Clients

Property Risk Data, Scattered Across Government and Commercial Sources

The Real Challenge: Data Reliability & Architecture

A Standalone MCP Server Built for AI Agents, in Weeks

Explore Custom MCP Architecture Services for PropTech

Property Intelligence Capabilities & Architecture

Technology Stack

Node.js, TypeScript

MCP, JSON-RPC 2.0

API-key (`X-API-Key`)

EPA, FCC, Satellite-ML, Noise

Claude Desktop, Cursor, Windsurf

Need Property Data or Risk Feeds Integrated into AI Agents?

What Our Clients Are Saying

Alexander Z Stanley

Rob Price

Jason Lorenti

Matthias Grdan

Marcos Queiroz

Relevant Case Studies

MCP Server Development Case Study | TechAvidus

Exposing Real Estate APIs to AI Agents

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Turning Scattered Property-Risk Feeds Into One AI-Ready Server for PropertyScoop

Our Clients

Property Risk Data, Scattered Across Government and Commercial Sources

The Real Challenge: Data Reliability & Architecture

A Standalone MCP Server Built for AI Agents, in Weeks

Explore Custom MCP Architecture Services for PropTech

Property Intelligence Capabilities & Architecture

Technology Stack

Node.js, TypeScript

MCP, JSON-RPC 2.0

API-key (X-API-Key)

EPA, FCC, Satellite-ML, Noise

Claude Desktop, Cursor, Windsurf

Need Property Data or Risk Feeds Integrated into AI Agents?

What Our Clients Are Saying

Alexander Z Stanley

Rob Price

Jason Lorenti

Matthias Grdan

Marcos Queiroz

Relevant Case Studies

MCP Server Development Case Study | TechAvidus

Exposing Real Estate APIs to AI Agents

Social Media Marketing for Pharmacy Recruitment

Schedule A Free Expert Session

Experts You Can Trust

Start a Conversation

Turning Scattered Property-Risk Feeds Into
One AI-Ready Server for PropertyScoop

API-key (`X-API-Key`)