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datasets_airbnb_hosts_facets

Return distribution counts for Airbnb host dataset facets such as superhost, verification, location, and listing count while applying search filters to analyze host segments.

Instructions

Facet the Airbnb hosts dataset. Returns distribution counts over the Airbnb hosts dataset (dataset id enum value airbnb-hosts), honoring the same filters as search. Facet enum: superhost, identity_verified, listing_count_band, hosting_years_band, interest, verification, location.

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": {
      "facet": "<facet>"
    },
    "name": "datasets_airbnb_hosts_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Full-text query over host name and bio, max 256 characters
`facet`	Yes	Facet enum: superhost, identity_verified, listing_count_band, hosting_years_band, interest, verification, location
`interest`	No	Exact interest as returned by the interest facet, max 128 characters
`location`	No	Free-text match over the profile location, max 128 characters
`min_reviews`	No	Minimum total guest reviews received, 0 or greater
`is_home_host`	No	Filter on the home-host flag
`is_superhost`	No	true keeps only Superhosts, false keeps only hosts without the badge
`max_listings`	No	Maximum number of listings the host manages, 0 or greater
`min_listings`	No	Minimum number of listings the host manages, 0 or greater
`verification`	No	Exact verification as returned by the verification facet, max 128 characters
`identity_verified`	No	true keeps only hosts whose identity is verified
`min_hosting_years`	No	Minimum years of hosting, 0 or greater

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

B3.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the return shape ('distribution counts'), which is the most important behavioral fact for a facet tool, but it says nothing about result limits, ordering, whether counts are exact or approximate, or whether calling without a query facets the entire dataset.

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?

Three front-loaded sentences with no filler, and the return-shape statement comes early. The only waste is restating the full facet enum list, which the schema already enumerates.

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

Completeness3/5

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

There is no output schema, so the description must explain return values; 'distribution counts' is accurate but thin for a 12-parameter aggregation tool — it doesn't say whether one facet or several are returned, whether results are sorted, or how many buckets appear. Adequate to call correctly, but not complete.

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

Parameters3/5

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

Schema description coverage is 100%, so all 12 parameters are already documented in structured form; baseline is 3. The description's enumeration of the facet enum duplicates the schema enum exactly and adds no syntax, constraint, or interaction detail (e.g. that values returned by the `interest`/`verification` facets feed the corresponding filter params).

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

Purpose4/5

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

States a specific verb ('Facet') and resource ('the Airbnb hosts dataset') and clarifies the output is distribution counts rather than records, which is the key distinction from datasets_airbnb_hosts_search. It stops short of naming siblings explicitly, but the aggregation framing makes the tool's role unambiguous.

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?

'Honoring the same filters as search' tells the agent the filter params carry over from the search tool, which is useful implied guidance. However there is no explicit when-to-use statement (e.g. 'use this to enumerate valid values before filtering', or 'do not use for retrieving host records'), and no stated alternative.

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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