realtystack-mcp

U.S.

CommunityOnlineNo sign-inGlobalFreeRead-only

What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    U.S. real-estate data: property records, AVM value + rent estimates, sale/rental listings.

    How to connect

    How to connect realtystack-mcp to Claude

    The server address to paste into Claude settings:

    https://realtystack-mcp.vercel.app/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect realtystack-mcp to ChatGPT

    The server address to paste into ChatGPT settings:

    https://realtystack-mcp.vercel.app/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect realtystack-mcp to Cursor

    The server address to paste into Cursor settings:

    https://realtystack-mcp.vercel.app/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (6)

    Raw names from tools/list. Only developers need these.

    search_propertiesSearch normalized U.S. property records (owner, structural attributes, tax assessments, tax + sale history) by location. Pin a location one of three ways: a full address, a city+state(+zipCode), or a latitude+longitude point with a radius (miles). All other parameters narrow the result set. Records are cached 24h upstream, so meta.cached may be true on repeat queries.
    get_propertyFull detail for a single property record, keyed by its RentCast id (the same id returned on every search_properties, avm_value, avm_rent, and listings record). Returns 404 if no property matches the id. Cached 24h upstream.
    avm_valueRun RentCast's automated valuation model for a subject property and return a point price estimate, a priceRangeLow/priceRangeHigh band, the resolved subjectProperty, and comparables[] — recent comparable sales each scored with a distance (miles) and correlation (0-1 similarity). Pin the subject with either address or latitude+longitude; when subject attributes are unknown, supply propertyType/bedrooms/bathrooms/squareFootage (or leave lookupSubjectAttributes true to have RentCast fill them). Cached 6h upstream.
    avm_rentRun RentCast's automated model for long-term (12-month lease) rent and return a point rent estimate, a rentRangeLow/rentRangeHigh band, the resolved subjectProperty, and comparables[] — recent comparable rentals each scored with distance and correlation. Same subject-pinning rules as avm_value (address or latitude+longitude, plus optional subject attributes). Cached 6h upstream.
    search_sale_listingsReturn a page of for-sale listings (list price, status, days on market, MLS name/number, listing agent + office contact, and price/status history) for a location. Same location-pinning rules as search_properties (address, city+state(+zipCode), or latitude+longitude+radius). Each record is tagged listingKind:"sale". Listings move faster than records, so these are cached only 30 minutes upstream.
    search_rental_listingsReturn a page of long-term (12-month lease) rental listings — same record shape as search_sale_listings (monthly price is the asking rent), but each record is tagged listingKind:"rental-long-term". Same location-pinning rules and 30-minute upstream cache as the sale-listings endpoint.