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datasets_apps_reviews_facets

Discover app-review facet values by store, app, country, or score, ordered by count, using the same filters as review search.

Instructions

Discover values in the app-reviews dataset. Returns up to 50 app-review facet buckets ordered by count, using the same filters as datasets_apps_reviews_search. Page and page_size do not change the aggregation. Facet enum: store, app_id, country, score.

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_reviews_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Full-text review query
`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 or android
`app_id`	No	Exact store app id
`country`	No	Storefront country
`min_score`	No	Minimum star rating, 1 through 5
`page_size`	No	Page size, default 20 and max 100

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?

With no annotations, the description carries the behavioral burden and does disclose key traits: a hard cap of 50 buckets, count-descending ordering, and the non-obvious fact that page/page_size do not affect the aggregation. It omits permission/auth expectations and whether counts are exact or approximate, keeping it short of a 5.

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

Conciseness5/5

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

Four short sentences, front-loaded with purpose and return shape, then filters, then the pagination caveat, then the enum. Nothing is redundant with the schema except the facet enum enumeration, which is cheap and useful.

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 9-parameter, no-output-schema, no-annotation tool, the description covers return shape, ordering, cap, filter delegation, and the pagination quirk. What a bucket actually contains numerically and whether counts are approximate or permission-scoped remain unstated, a minor but real gap.

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 the schema does not: the facet enum is restated inline and, more importantly, the note that pagination parameters are inert for aggregation corrects a likely misreading of "Result page, 1-based" / "Page size". It also tells the agent the remaining filters behave exactly as in the search tool.

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 and resource ("Discover values in the app-reviews dataset"), names the return shape (facet buckets ordered by count), and cross-references the sibling `datasets_apps_reviews_search` whose filters it shares. An agent can distinguish facet discovery from review retrieval without opening a schema.

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?

Usage is implied by "Discover values" plus the note that page/page_size do not change the aggregation, which signals this is for aggregation rather than result paging, and filter semantics are delegated to the search sibling. However, it never explicitly says when to prefer this over `datasets_apps_reviews_search` or when not to call it (e.g., to get actual reviews), leaving the routing to inference.

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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