ChatGPT Plugin

Microburbs

Australian Property Data

Brand: Unknown

Category: Pending
Primary Subcategory: Pending

Integration details

Description

Microburbs is Australia's most comprehensive and accurate provider of property and geospatial data. Established in 2013, it holds over 5 billion data points covering every Australian address, street and suburb, and it is the only provider of granular street- and mesh-block-level data. Ask about any suburb or property and get answers grounded in that data: back-tested price forecasts, median prices and rents, yields and days on market, crime compared with the national median, school catchments and rankings, flood and bushfire risk, zoning and development, and census demographics. Without an account, answers cover nine sample suburbs, one in each state and territory: Bondi, Belmont North, St Kilda (Vic.), Fortitude Valley, Subiaco, Unley, Sandy Bay, Kambah and Nightcliff.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Pending
Primary Subcategory: Pending
Secondary Subcategories: None listed
Brand: Unknown
Access: No account required
First tracked: 2026-10-02
Tool count: 173
Geography: US

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Primary Subcategory

Competitive lineup

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Tools

173 tools agents can invoke

Returns immediately if the API is up. No authentication required and not metered — safe to poll from a load balancer, uptime monitor, or status dashboard without affecting your usage. **Free.**
ping
Return key identity and the authoritative Autumn credit balance. **Free.**
me
Every property-level risk overlay in one call. Flat 30¢ — may grow without increasing the bundle price as new overlays are added. With ``?geojson=true`` each risk that has a polygon overlay also carries its 1km-radius-clipped `geojson` FeatureCollection + `geojson_meta`. **Price: 48¢ per call.**
properties_risks_all
List the regions that exist at a level — every suburb, LGA, postcode, etc. Free (0c). Use it to discover the exact region names to pass to /values. **Free.**
area_stats_regions
Discover which statistical fields exist. Free (0c). Fuzzy, token-based matching covers table topics and clean column labels as well as terse field codes. Common natural-language plurals and Census synonyms are handled, so the verbatim `query=couples with children` discovers the exact 2021 ABS family-composition count fields rather than substituting the broader `census_family_households` metric. Use the returned field code(s) with the priced `/area-stats/SAL/{suburb}/values` route. ~17k fields available. **Free.**
area_stats_fields
Statistical field values for one region. Pass `fields` — 1 to 5 field codes (from /fields) — to fetch. At SAL level, `mode=sa1` switches from the suburb-aggregate value to a per-SA1 breakdown — every SA1 that falls inside the suburb, each with its own values. **Price: 3¢ per call.**
area_stats_values
Same-SA3 comparable-suburb candidates (canonical SAL names).
suburbs_market_comparable_suburbs
How many suburbs match the filter, without paying per row. 5c. Use this to size a result before searching: /search is capped at 25 rows per call, so `total` here tells you how many `offset` pages you need. **Price: 5¢ per call.**
suburbs_finder_count
Suburb and national total-crime medians (predicted incidents per 100,000 residents per year) with a vs-national verdict.
suburbs_crime_summary
Resolve a freeform address string to candidate G-NAF identifiers. Use this before calling any ``/v1/properties/{gnaf_id}/...`` route — those endpoints require a GNAF in the path. Returns up to 10 candidates, ranked by Meilisearch score. **Price: 1¢ per call.**
geocode_address
Autocomplete a partial suburb search string against the ABS SAL list. Use this before calling ``/v1/suburbs/{name}/...`` — those endpoints require a canonical SAL name in the path. **Free.**
geocode_suburb
LGA's parent chain — state + GCCSA. LGAs aren't strictly nested in SA4 so the upward chain stops there. **Price: 3¢ per call.**
lga_profile
All Australian LGAs. Optional `?state=` filter (e.g. 'NSW'). Returns `{lga, state}` per row. Flat cost. **Price: 3¢ per call.**
lga_list
All Australian SA4s. Optional `?state=` filter. Returns `{sa4, state}` per row. Flat cost. **Price: 3¢ per call.**
sa4_list
Mesh-block codes inside a suburb. ``sal`` is required for now — nationwide / by-LGA / by-SA3 listings will land later when there's a story for the large payloads (a single LGA can hit a few thousand mesh blocks). **Price: 3¢ per call.**
mbs_list
List Australian suburbs (SALs). Always returns `{suburb, state}` per row so SAL-name collisions across states are unambiguous. With no filter you get the first 1000 suburbs alphabetically (browser-renderable preview). Pass any of the filters to narrow: state / lga / sa4 / sa3 / postcode / q (name search). Filters combine (AND). Flat cost regardless of how many rows come back. **Name search (`q`) is typo-tolerant.** It tries an exact match, then substring, then a fuzzy near-miss, and returns `match` (`exact` / `contains` / `fuzzy`) and `score` on every row so you can see which happened. **Always tell the user which suburb you resolved to, and its state, before quoting numbers for it.** A fuzzy match is a suggestion, not a confirmation — `rokeby` is one letter from Kokeby in Western Australia and Rokeby exists in both Tasmania and Victoria. If more than one candidate is plausible, ask rather than pick. When `q` is combined with `state`, the state is a **preference, not a filter**: in-state candidates rank first, but a suburb of that name in another state is still returned rather than hidden, so a near-miss becomes "Seaview is in Victoria, not Tasmania" instead of "no data". Every other filter stays a strict AND. **Free.**
suburbs_list
The mesh block's upward ABS chain — SA1, SA2/3/4, GCCSA, state — plus the parallel admin chain (LGA, suburb (SAL), postcode). **Price: 3¢ per call.**
mbs_profile
Neighbouring suburbs as a GeoJSON FeatureCollection — each with its boundary polygon and distance in km.
suburbs_shapes_neighbours
Nearest neighbouring suburbs with distance in km, closest first.
suburbs_hero_neighbours
Every named venue / amenity in the suburb as GeoJSON Point features, plus a per-category count. Each point carries `name`, a broad `category` (Restaurant / Cafe, Shopping, Health, Entertainment, Sport & Fitness, Education, …) and a finer `subtype` (e.g. `pub`, `bar`, `cafe`, `restaurant`, `gym`, `supermarket`, `school`, `pharmacy`). This is the endpoint for "what / how many <venue type> are in this suburb" and "name the pubs / cafes / gyms in X" — filter or count the features on `subtype`, and read real venue names from `name`. The top-level `categories` array gives the broad-category counts without walking the list. Unpaged by default. Big suburbs are big — Sydney is ~3.7 MB of features — so pass `limit`/`offset` (or narrow with `category`) when you don't need them all. `count` and `categories` always describe the whole suburb, so paging never hides the totals. Flat price per call regardless of page size.
suburbs_lifestyle_pois
Resolve a GNAF to its full ABS Statistical Geography Standard hierarchy (Mesh Block → SA1 → SA2 → SA3 → SA4 → GCCSA → State) plus the parallel admin chain (LGA, suburb (SAL), postcode). Call this once to get every ABS key the rest of the data layers index on. **Price: 3¢ per call.**
properties_profile
Modelled property value over time (monthly for the last year, annual before that). Houses only — units have no series. **Price: 30¢ per call.**
properties_valuation_value_series
Microburbs ML AVM for a single GNAF — point estimate, 80% range, plus a confidence score (0–100) derived from the interval width. **Price: 10¢ per call.**
properties_valuation_avm
Quoting accuracy of the property's current listing agency — cohort, median guide-vs-sold deviation, underquote rate and its 5 most-recent tracked sales. Only while the property is listed for sale with a tracked agency. **Price: 50¢ per call.**
properties_valuation_agent_quoting
**Price: 23¢ per call.**
properties_basics_all
**Price: 74¢ per call.**
properties_comparables_all
**Price: 8¢ per call.**
properties_development_all
**Price: 80¢ per call.**
properties_history_all
**Price: 105¢ per call.**
properties_schools_all
**Price: 10¢ per call.**
properties_valuation_all
Bathroom count from the latest matching listing. **Price: 5¢ per call.**
properties_basics_bath_count
Bedroom count from the latest matching listing. **Price: 5¢ per call.**
properties_basics_bed_count
The subject's bedroom band (bed count from listing history) and the suburb's 10-year price series for that band. **Price: 15¢ per call.**
properties_context_street_bedroom_series
Bushfire-prone-area overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is in a bushfire zone". **Price: 15¢ per call.**
properties_risks_bushfire
Top comparable sales used to triangulate the subject property's value. **Price: 60¢ per call.**
properties_comparables_cma_comp_set
Single best comparable from the CMA set (highest dist_score). **Price: 10¢ per call.**
properties_comparables_cma_single_comp
Council land valuations for the parcel — val_2021..val_2025 series with YoY change and $/sqm, plus lot area and zoning description. **Price: 15¢ per call.**
properties_valuation_land_values
Development overlays intersecting the property's parcel. **Price: 5¢ per call.**
properties_development_zoning_overlays
Dwelling type (house, unit, townhouse, ...). **Price: 5¢ per call.**
properties_basics_dwelling_type
Environmental-risk overlay (contamination, salinity, ...). **Price: 3¢ per call.**
properties_risks_environment
Coastal / soil erosion overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is eroding". **Price: 3¢ per call.**
properties_risks_erosion
Modelled flood overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property floods". **Price: 10¢ per call.**
properties_risks_flood
Heritage-listing overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is heritage-listed". **Price: 3¢ per call.**
properties_risks_heritage
High-voltage power-line easement near the parcel. **Price: 2¢ per call.**
properties_risks_hv_power_line
Recorded historical flood extents within 1 km. Read `on_property` — a returned row means "found nearby", not "this property flooded". **Price: 3¢ per call.**
properties_risks_historical_flood
Parcel land area in square metres from the cadastral feed. **Price: 3¢ per call.**
properties_basics_land_area_sqm
Landslide-risk overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is at landslide risk". **Price: 3¢ per call.**
properties_risks_landslide
Schools within ~5km of the property, ranked by NAPLAN / socioeconomic score. **Price: 100¢ per call.**
properties_schools_ranking
Mesh-block polygons within 1 km of the property (GeoJSON), each with avg annual growth, investor counts, modal street and median sold price. **Price: 30¢ per call.**
properties_context_mesh_blocks
Mobile-coverage black-spot overlay near the parcel. **Price: 2¢ per call.**
properties_risks_mobile_black_spot

FAQs

Microburbs FAQ

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Read the methodology

How do I improve a ChatGPT Plugin's discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.

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Competitive lineup

173 tools agents can invoke

Microburbs FAQ