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datasets_airbnb_search

Search aggregate Airbnb short-term rental market data by country, market, room type, or property type, with filters for ratings, superhosts, and guest favorites.

Instructions

Search the Airbnb markets dataset. Returns aggregate Airbnb short-term-rental market rollups from the dataset id enum value airbnb-markets. Aggregate-only: each row is a market cell, never an individual listing. Thin cells are suppressed. group_by enum: country, market, 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). Enrichment-derived groupings include a coverage object with the number and share of filtered listings carrying that dimension; the 20% minimum gate is not a completeness guarantee, and counts still describe classified rows only. Each cell also carries median_price_usd, the median nightly price converted to USD via an approximate dated FX snapshot, for cross-country comparison (combine with group_by=room_type for median price by room type); guest_favorite_pct, the share of listings carrying the Guest Favorite badge (an observed lower bound, like superhost_pct); and avg_person_capacity, the average guests a listing sleeps over the detail-page-enriched sample. Sort enum: listings_desc, superhost_pct_desc, rating_desc, key_asc. Billing: this search costs 1 credit per record returned, with a minimum of 2 (an empty result costs 2, a full page of 100 costs 100).

Input Schema

NameRequiredDescriptionDefault
pageNoPage number, defaults to 1
sortNoSort enum: listings_desc, superhost_pct_desc, rating_desc, key_asc
marketNoExact metro-market filter, e.g. Paris, 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
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
superhostNoCount only Superhost listings
min_ratingNoMinimum listing rating, from 0 through 5
active_sinceNoFreshness filter, an ISO-8601 date (YYYY-MM-DD); only listings last seen on or after it are counted
min_listingsNoMinimum listings per cell; raises the small-cell suppression floor (never lowered below the built-in minimum)
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 / group_by / enum
      Added value: +[
      +  "country",
      +  "market",
      +  "admin1",
      +  "locality",
      +  "room_type",
      +  "property_type"
      +]
    • addedInput schema / properties / sort / enum
      Added value: +[
      +  "listings_desc",
      +  "superhost_pct_desc",
      +  "rating_desc",
      +  "key_asc"
      +]
  2. Changed2 schema fields changedv1.5.0
    • 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

A4.4/5.0
Behavior4/5

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

With no annotations the description carries full burden and does well: it discloses thin-cell suppression, coverage objects with a 20% gate that is 'not a completeness guarantee,' observed-lower-bound metrics, approximate FX conversion, and explicit billing (1 credit/record, min 2). It does not state auth/rate limits, but the behavioral caveats are unusually thorough for a search tool.

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-loads purpose, resource, and the aggregate-only constraint before enumerating enums and metrics. It is dense and long, but nearly every clause adds actionable information rather than restating the schema.

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

Completeness5/5

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

For a 12-parameter tool with no annotations and no output schema, the description covers what the tool does, how grouping/sorting shape results, the shape and caveats of returned metrics, and the credit cost — everything an agent needs to call it correctly.

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 baseline is 3, but the description adds real meaning beyond the schema: it defines admin1/locality/room_type/property_type semantics, and explains output metrics like median_price_usd, guest_favorite_pct (observed lower bound), and avg_person_capacity.

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?

States a specific verb (search) and resource (Airbnb markets dataset) and clarifies scope emphatically: 'aggregate Airbnb short-term-rental market rollups… Aggregate-only: each row is a market cell, never an individual listing.' This distinguishes it clearly from the listing-level airbnb_search sibling.

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

Usage Guidelines4/5

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

Explains it returns market-level aggregates and details how group_by/sort shape the query, implicitly steering market-analysis use cases vs listing lookups. However it never names an explicit alternative (e.g. airbnb_search/airbnb_room) or states when-not to use it, leaving that inference to the reader.

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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