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datasets_apps_facets

Discover app dataset facet values such as store, category, country, developer, and platform. Filter by query, rating, reviews, and free status to analyze app market segments.

Instructions

Discover values in the apps dataset. Returns up to 50 allowlisted app-facet buckets ordered by count, using the same filters as datasets_apps_search. Page and page_size do not change the aggregation. Facet enum: store, platforms, category, country, developer, free. Store enum: ios, android, both. Platform enum: phone, pad, tablet, mac, tv, chromebook, watch, vision, xr, car, windows.

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_apps_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Full-text query over app title, developer and category, max 256 characters
`free`	No	Filter by free/paid status
`page`	No	Result page, 1-based
`sort`	No	Validated search sort; bucket order stays count-desc, and time-series date_desc keeps its search history scope
`facet`	Yes	Facet field
`store`	No	Store filter: ios, android, both
`country`	No	Exact storefront country filter, max 128 characters
`category`	No	Exact app-store category filter, max 128 characters
`developer`	No	Exact developer/publisher name filter, max 128 characters
`page_size`	No	Page size, default 20 and max 100
`platforms`	No	Repeatable device-platform filter
`min_rating`	No	Minimum store rating, 0 through 5
`min_reviews`	No	Minimum ratings/review count, 0 or greater

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are supplied, so the description carries the full burden, and it does substantial work: cap of 50 buckets, allowlisted facets only, count-descending bucket order, shared filter semantics with search, and the fact that paging inputs have no effect. It does not state the operation is read-only, nor how a bucket is shaped beyond 'value + count by ordering'.

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

Conciseness3/5

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

The functional statements are front-loaded and tight, but roughly a third of the text is a verbatim restatement of the facet, store, and platform enum values already encoded in the schema, which dilutes rather than earns its place.

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 13-parameter read tool with no annotations and no output schema, the description covers output size, ordering, filter parity with search, and paging behavior — enough for correct invocation. It stops short of describing the bucket payload structure or confirming that no authentication/pagination is involved.

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 beyond the schema by clarifying that page and page_size have no effect on the aggregation — something the schema's 'Result page, 1-based' wording would otherwise mislead an agent about. It also repeats the facet/store/platform enum values already present in the schema, which adds no new information.

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?

The description opens with a specific verb and resource ('Discover values in the apps dataset') and immediately pins down the output as up to 50 allowlisted app-facet buckets, which distinguishes it from the sibling search tools (datasets_apps_search) and from charts/reviews facet tools.

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?

It states that the same filters as `datasets_apps_search` apply, which tells an agent how to combine this with search-style filtering, and it warns that page/page_size do not change the aggregation. It never says explicitly when to prefer this over running a search, and offers no exclusions, so it falls short of a full when/when-not pairing.

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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