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datasets_airbnb_facets

Get suppressed distribution counts for Airbnb listings by country, market, room type, or other facets, with filters like rating and superhost to narrow results.

Instructions

Facet the Airbnb markets dataset. Returns suppressed distribution counts over the Airbnb markets dataset, honoring the same filters as search. Facet enum: country, market, currency, superhost, guest_favorite, rating_band, review_band, admin1 (top subdivision), locality (settlement), room_type (entire_place/private_room/hotel/shared_room), property_type (Airbnb's canonical listing type from the detail page), amenities (each amenity with the count of listings offering it). The admin1, locality, room_type, property_type and amenities facets stay empty until their enrichment coverage is high enough to be reliable. group_by enum: country, market, admin1, locality, room_type, property_type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
facetYesFacet enum: country, market, currency, superhost, guest_favorite, rating_band, review_band, admin1, locality, room_type, property_type, amenities
marketNoExact metro-market filter, max 128 characters
countryNoExact ISO-3166-1 alpha-2 country filter, e.g. FR
group_byNoAggregate cell dimension enum: country, market, admin1, locality, room_type, property_type. Defaults to country
superhostNoCount only Superhost listings
min_ratingNoMinimum listing rating, from 0 through 5
active_sinceNoFreshness filter, an ISO-8601 date (YYYY-MM-DD)
min_listingsNoMinimum listings per bucket; raises the small-cell suppression floor
guest_favoriteNoCount only Guest Favorite listings (an observed lower bound; the badge under-counts)
min_review_countNoMinimum listing review count, 0 or greater

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "country",
+  "market",
+  "currency",
+  "superhost",
+  "guest_favorite",
+  "rating_band",
+  "review_band",
+  "admin1",
+  "locality",
+  "room_type",
+  "property_type",
+  "amenities"
+]
    • addedInput schema / properties / group_by / enum
      Added value: +[
+  "country",
+  "market",
+  "admin1",
+  "locality",
+  "room_type",
+  "property_type"
+]
  2. Changed3 schema fields changedv1.5.0
    • changedInput schema / properties / facet / description
      Previous value: -"Facet enum: country, market, currency, superhost, rating_band, review_band"New value: +"Facet enum: country, market, currency, superhost, guest_favorite, rating_band, review_band, admin1, locality, room_type, property_type, amenities"
    • changedInput schema / properties / group_by / description
      Previous value: -"Aggregate cell dimension enum: country, market. Defaults to country"New value: +"Aggregate cell dimension enum: country, market, admin1, locality, room_type, property_type. Defaults to country"
    • addedInput schema / properties / guest_favorite
      Added value: +{
+  "description": "Count only Guest Favorite listings (an observed lower bound; the badge under-counts)",
+  "type": "boolean"
+}
  3. Addedv1.2.0

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden, and it does well: it discloses suppression behavior, filter inheritance from search, and the important caveat that certain facets remain empty until enrichment coverage is reliable. It does not discuss rate limits or auth, but 'returns counts' makes the read-only nature reasonably clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the operation and output type, followed by well-organized enum lists with inline clarifications. The main redundancy is repeating 'the Airbnb markets dataset' in the first two sentences, but each sentence otherwise contributes useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter tool with no annotations and no output schema, this description covers the operation, filter alignment, facet semantics, suppression, and empty-facet behavior. It omits details like output shape or pagination, but 'distribution counts' plus the schema provides enough for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: it explains the facet enum values (e.g., admin1 as top subdivision, room_type canonical values, amenities as counts) and the group_by options. The 'same filters as search' clause also clarifies the role of all filter parameters without repeating the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb and resource ('Facet the Airbnb markets dataset') and immediately states the result: suppressed distribution counts. The phrase 'honoring the same filters as search' also signals how this differs from the sibling search tool, so an agent can tell aggregation from raw listing retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context ('honoring the same filters as search') but never states when to prefer this tool over siblings like datasets_airbnb_search or datasets_airbnb_item, nor any exclusion cases. The aggregation-vs-search distinction is implied rather than explicit, which is a moderate gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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