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MCP Integration Guide

Use Stash as a structured data layer for AI agents, copilots and automation workflows. Combine property, suburb, market and risk data into real decision-ready workflows.

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Supported by Claude. More AI tools coming soon

Coming April 2026

Use Stash as a structured data layer for AI agents, MCP tools and automation workflows — powering property decisions with connected property, suburb, market and risk data.

Get started with AI workflows

Stash plugs directly into AI agents and MCP workflows. Most implementations follow this pattern:

  1. Resolve a property, suburb or region via suggest endpoints
  2. Fetch property or suburb context
  3. Fetch supporting market, planning and statistics data
  4. Combine responses into a structured JSON object
  5. Pass into AI or workflow logic to generate an answer, report or action

Property decision workflow

User asks

Should I buy this property?

System calls

GET  /properties/suggest
GET  /properties/{propertyId}/_detailed
GET  /properties/{propertyId}/avm
POST /market-activity/sales
GET  /properties/{propertyId}/risk-overlays
GET  /properties/{propertyId}/planning-info

Stash returns

  • Detailed property attributes and context
  • Estimated value + confidence
  • Comparable sales and market activity
  • Risk overlays, hazards and planning information
  • Connected data ready for AI-driven decisioning

Result

A structured, explainable recommendation or report that can be used in AI assistants, internal workflows or client-facing outputs.

Example structured context

{
  "property": {
    "source": "GET /properties/{propertyId}/_detailed"
  },
  "valuation": {
    "source": "GET /properties/{propertyId}/avm"
  },
  "sales": {
    "source": "POST /market-activity/sales"
  },
  "planning": {
    "source": "GET /properties/{propertyId}/planning-info"
  },
  "risk": {
    "source": "GET /properties/{propertyId}/risk-overlays"
  }
}

Example output

The property is supported by comparable sales and sits in a suburb with strong recent market performance. No major risk overlays were identified. Based on available data, this appears to be a suitable investment opportunity.

Core MCP tools & outputs

suggest_entity

Resolve user input into property, suburb or region IDs

GET /properties/suggest
GET /suburbs/suggest
GET /regions/suggest
get_property_profile

Fetch property details and context

GET /properties/{propertyId}
GET /properties/{propertyId}/_basic
GET /properties/{propertyId}/_extended
GET /properties/{propertyId}/_detailed
get_property_avm

Return estimated value and confidence

GET /properties/{propertyId}/avm
get_market_activity

Return sales, listings or rental market activity

POST /market-activity/sales
POST /market-activity/listings
POST /market-activity/rentals
get_suburb_context

Fetch suburb profile, statistics and demographics

POST /suburbs
GET /suburbs/{localityId}
GET /suburbs/{localityId}/statistics
GET /suburbs/{localityId}/demographics
GET /suburbs/{localityId}/timeseries
get_planning_and_risks

Fetch planning info, hazards and risk overlays

GET /properties/{propertyId}/risk-overlays
GET /properties/{propertyId}/planning-info
GET /properties/{propertyId}/hazards
generate_property_report

Generate structured reports or outputs

GET /generateReport

Best practices

  • Always start with suggest endpoints to resolve the right property, suburb or region
  • Use /_detailed only when richer property context is required
  • Combine property, AVM, market activity and planning data for stronger AI outputs
  • Use suburb statistics and timeseries to support market-level explanations
  • Pass structured JSON into AI models instead of raw text
  • Keep outputs explainable by linking recommendations back to underlying Stash data

Use cases

  • AI property assistants using property, AVM and planning endpoints
  • Automated CMA workflows using market activity and suburb statistics
  • Suburb research copilots using profile, demographics and timeseries data
  • Deal qualification workflows using risks, hazards and comparable sales
  • CRM enrichment using property, suburb and market context
  • Automated reports using GET /generateReport

Start building with Stash

Begin with property lookup, then layer valuation, sales and suburb insights.

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