Get Property Condition
get_property_conditionWhat it returns
An AI condition assessment for a US residential property — overall condition category (Excellent/Good/Fair/Poor), confidence score, per-room breakdown when photos are available, and any flags (water damage, deferred maintenance signals). Built from a computer-vision pipeline on the listing's photo set.
When to use it
Filter MLS feeds to move-in-ready vs. fixer candidates.
Adjust AVM expectations based on actual condition.
Pre-screen photos to skip a manual review pass.
Pricing
2 credits.
FAQ
What model is used?
A multimodal AI vision pipeline; specific models aren't disclosed. Outputs are described in domain terms (Excellent, Good, …) so the contract is stable across model swaps.
Does it work on listings without photos?
No — returns null overall and a low confidence score. To supply your own photos for a more accurate assessment, use property-condition-custom-photos.
Tool call template
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"property_address": "<property_address>"
},
"name": "get_property_condition"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| property_address | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||