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datasets_steam_reviews_facets

Discover Steam review facet counts by app ID, language, or recommendation with search filters; returns up to 50 buckets ordered by count for distribution analysis.

Instructions

Discover Steam review values. Returns up to 50 Steam review facet buckets ordered by count, using the same filters as datasets_steam_reviews_search. Page and page_size do not change the aggregation.

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_steam_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
`app_id`	No	Exact Steam app id
`language`	No	Review language
`voted_up`	No	Recommendation filter
`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 disclosure burden and does well: it states the 50-bucket cap, that ordering is count-desc regardless of the sort parameter, and that page/page_size do not alter the aggregation. It omits anything about authentication, rate limits, or what a bucket contains (value plus count), which keeps 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?

Two sentences, zero filler, with the core behavior (facet buckets, capped at 50, count-ordered) front-loaded and the pagination caveat following. Every clause conveys operational information.

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 an 8-parameter aggregation tool with no annotations and no output schema, the description covers purpose, ordering, cap, and the surprising non-effects of paging and sort. The main remaining gap is return shape — it never states that buckets pair a facet value with a count — but the phrasing 'facet buckets ordered by count' largely conveys that.

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: page and page_size are declared irrelevant to the aggregation, and the sort parameter's effect on bucket ordering and on date_desc history scope is clarified beyond the schema's own note. The facet enum and filter semantics are left entirely to the schema.

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 names a specific operation on a specific resource: facet buckets over Steam reviews, with an explicit cap of 50 and count-desc ordering. It also explicitly contrasts itself with `datasets_steam_reviews_search` by saying it applies the same filters, so an agent can distinguish aggregation from record 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?

The reference to `datasets_steam_reviews_search` filters implies this tool is the aggregation counterpart to that search, which is useful implied guidance. However, it never states the selection condition outright (e.g. 'use this to count reviews by language instead of listing them') and gives no exclusions or guidance versus other aggregation siblings such as `steam_reviews_histogram`.

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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