Real estate data for AI agents: data layers and MCP

A real estate data layer for AI is a service that sources public property records (parcels, zoning, permits, ordinances, meeting minutes) on demand, stores them in a queryable form, keeps them current, and lets AI assistants like Claude and ChatGPT query them through an MCP connector, with every answer traced to its source.

AI assistants like Claude and ChatGPT are becoming agents: instead of only answering questions, they take on tasks, research, analyze, and act for you in plain English. More and more real estate teams use them in their daily work.

Ask one for every parcel over 20 acres zoned for agriculture in a county, though, and it will usually do one of three things: search the web and summarize whatever loads, stall on a county portal, or guess. The model is capable. The data is the hard part.

Doing it yourself is slower still: find the county's GIS portal, download parcels and zoning, line them up, and filter, then repeat for the next county. That is hours per county, and the work does not carry over to the next question.

The problem

US real estate data is spread across thousands of county and city systems. Zoning is set by municipalities, permits live in portals, and rezoning history lives in meeting minutes. Much of it sits behind bot protection or in PDF exports. An assistant that has to rebuild a dataset from scratch for every question is slow, and it has little way to check its own work.

What a data layer does

A real estate data layer sits between those sources and your assistant:

1. Sources on demand. It finds and pulls the datasets in the counties you work in, and anything new your assistant asks for.
2. Cleans and stores. It normalizes formats, joins datasets to parcels, and keeps everything in one place so you do not have to run your own database.
3. Keeps it current. It refreshes datasets as sources change.
4. Answers with sources. Every row points back to the record it came from.

Why it matters for agents

With a data layer underneath, an assistant queries instead of rebuilding. Questions that took hours of portal work become a query that returns in seconds, and the next question builds on the same data. Teams keep the assistant they already use, whether that is Claude, ChatGPT, Grok, or their own agent, and plug the data layer in.

Where Ploti fits

Ploti is the real estate data layer for AI. It works across the whole range of local records: county and city GIS servers, permit portals, recorders, assessors, and planning and commission records, including sources that block automated access and records a county only shares on request. Everything it fetches is stored so agents can query the full dataset, kept current, and traced to its source. You use it through your own assistant with an MCP connector, for example in Claude or ChatGPT, or through Ploti's own agent.

It also works differently from a traditional data provider:

• Self-serve. There is no contract to negotiate; you sign up and start with a 7-day trial.
• You pay for the places you work. You choose your coverage areas and pay per county, starting at $50 a month, so the cost is tens or hundreds of dollars a month rather than an annual data contract that can run into the tens of thousands.
• Full access, not gated access. Your assistant can query every dataset in your counties in full.
• It builds up over time. Ploti keeps pulling data in your counties without being asked, so more of what you need is ready before you need it. Anything still missing, you can request.

Frequently asked questions

Can ChatGPT or Claude access real estate data?

On their own, they search the web and summarize what loads, which fails on county portals, PDFs, and bot-protected sites. Connected to a real estate data layer like Ploti through an MCP connector, they can query parcels, zoning, permits, and ordinances directly, with sources.

What is an MCP server for real estate data?

MCP (Model Context Protocol) is the standard AI assistants use to connect to outside tools and data. A real estate MCP server lets an assistant such as Claude, ChatGPT, Gemini, or Cursor query property data instead of searching the web for it.

Where does the data come from?

From official public sources: county and city GIS servers, permit portals, recorders, planning and commission records, and state and federal datasets. Each answer points back to the source it came from.

What if the data I need is not available yet?

Ask for it. Ploti sources it, including from hard-to-reach portals, and can email a county for records it does not publish online. Once fetched, it stays available to query.