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datasets_apps_reviews_facets

Read-only

Return up to 50 facet buckets with counts for one app-review field using the filters from datasets_apps_reviews_search. facet accepts store, app_id, country, or score; page and page_size do not change counts; sort is validated but does not change bucket order. Use datasets_apps_reviews_search to find review records.

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

NameRequiredDescriptionDefault
qNoOptional full-text query over review text, title and author, max 256 characters.
pageNoResult page number, 1-based, default 1; page times page_size must not exceed 10000.
sortNoOptional sort order. Allowed values: recent, score_desc, score_asc, helpful_desc. Defaults to recent.
facetYesrequired,Required facet. Allowed values: store, app_id, country, score.
storeNoOptional store filter. Allowed values: ios, android.
app_idNoOptional exact app filter — the iOS numeric track id or the Android package name, max 128 characters.
countryNoOptional storefront country filter, max 128 characters, e.g. us.
min_scoreNoOptional minimum star rating, from 1 through 5.
page_sizeNoPage size, default 20, max 100; page times page_size must not exceed 10000.

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations only declare readOnlyHint/openWorldHint, so the description carries the rest — and it delivers real behavioral detail: a hard cap of 50 buckets, and the non-obvious fact that page, page_size and sort do not affect counts or bucket order. That prevents wasted calls. No mention of auth or error behavior, keeping it below 5.

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 sentences, front-loaded with the return shape, then the no-effect caveat, then the alternative-tool pointer. Efficient. The inline list of facet values duplicates the schema's allowed values, a minor redundancy.

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?

An output schema exists, so return values need no explanation; the description supplies what the schema cannot — the 50-bucket cap and the fact that pagination/sort are no-ops for counts. For a 9-parameter, fully documented, read-only aggregation tool, nothing an agent needs is missing.

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; the description earns an extra point by explaining which parameters are semantically inert here (page, page_size, sort) and by tying the filter parameters back to the sibling search tool. The facet value enumeration largely restates the schema rather than adding to it.

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: 'Return up to 50 facet buckets with counts for one app-review field.' It explicitly scopes the operation (one field at a time, capped at 50) and names the sibling tool whose filters it reuses, so it is distinguishable from datasets_apps_reviews_search and datasets_apps_reviews_item without opening any schema.

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 routes the agent: use this for facet counts, use datasets_apps_reviews_search 'to find review records.' That names the alternative and the condition selecting it. It stops short of an explicit exclusion (e.g. 'do not use this to retrieve review records'), so a 4 rather than a 5.

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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