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datasets_steam_charts_facets

Discover Steam chart facet buckets by count using search filters, so you can see top charts, countries, apps, or snapshot dates.

Instructions

Discover values in the Steam charts dataset. Returns up to 50 Steam chart-facet buckets ordered by count, using the same filters as datasets_steam_charts_search. Page and page_size do not change the aggregation. A snapshot_date facet without date spans all history; other facets use the search date scope, including the latest-snapshot default when applicable and full history for app_id or date_desc sort.

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_charts_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Full-text app name query
`date`	No	Snapshot date, yyyy-MM-dd
`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
`chart`	No	Chart enum: most_played, concurrent, top_sellers
`facet`	Yes	Facet field
`app_id`	No	Exact Steam app id
`country`	No	Storefront country for top sellers
`page_size`	No	Page size, default 20 and max 100

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations supplied, the description carries the full burden and does meaningful work: it discloses the 50-bucket cap, count-descending ordering, the fact that page/page_size do not alter the aggregation, and that sort does not reorder buckets. The date-scope rules (snapshot_date without date spans all history; app_id/date_desc sort gives full history; otherwise latest-snapshot default) are genuinely non-obvious behavior. It stops short of describing bucket contents or empty-result behavior.

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 purpose and return shape before the scoping caveats, with no filler. The final sentence is dense and slightly tangled ('including the latest-snapshot default when applicable and full history for app_id or date_desc sort'), but every clause carries information not available elsewhere.

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, unannotated aggregation tool, the description covers the aggregation contract and the tricky date-scope matrix, which are the highest-risk sources of misuse. It does not describe what a bucket actually contains (facet value plus count) or behavior on unknown facet values, leaving a modest 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 description coverage is 100%, so the baseline is 3, and the description earns above that by explaining cross-parameter semantics the schema cannot express: page/page_size are inert for the aggregation, sort is validated but bucket order stays count-desc, and facet choice changes the effective date scope. The only gap is that the interaction of `date` with a snapshot_date facet is stated but the `country`/`chart` scope is left implicit.

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 and resource ('Discover values in the Steam charts dataset') and pins down the return shape: up to 50 facet buckets ordered by count. It references the sibling `datasets_steam_charts_search` as the filter-equivalent tool, which helps an agent place it, but it never states the contrast directly (aggregated facet discovery vs. record listing), so differentiation is inferred rather than declared.

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 description ties filtering to `datasets_steam_charts_search` and notes that pagination does not affect the aggregation, which implicitly signals this is the tool for value enumeration rather than record retrieval. There is no explicit when-to-use/when-not statement and no guidance on choosing between facet fields for a given intent, leaving the agent to infer the selection rule.

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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