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datasets_steam_playercounts_facets

Discover Steam player-count facet buckets by app ID or snapshot date, ordered by count, and scope counts to a specific day using the same filters as search.

Instructions

Discover Steam player-count app and date values. Returns up to 50 Steam player-count facet buckets ordered by count, using the same filters as datasets_steam_playercounts_search. The search and snapshot_date facet cover stored history by default; supplying date scopes counts to that day. 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_playercounts_facets"
  }
}

Input Schema

Name	Required	Description
`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
`facet`	Yes	Facet field
`app_id`	No	Exact Steam app id
`page_size`	No	Page size, default 20 and max 100

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations at all, the description carries the full burden and does a fair job: it discloses the 50-bucket cap, count-desc ordering, the default history scope, and the non-obvious fact that paging does not change the aggregation. It omits return shape, empty-result behaviour, and any access/permission notes, which leaves a gap for a tool with zero annotation coverage.

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?

Four short sentences, front-loaded with what the tool returns, then scope behaviour, then the paging caveat. No sentence is filler, though the phrase 'Steam player-count facet buckets' is repeated and could be tightened slightly.

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 six-parameter, no-annotation, no-output-schema facet tool, the description covers the essentials: what a bucket is, how many, in what order, how `date` changes scope, and that paging is aggregation-neutral. The remaining gap is return-value structure and any note on an empty facet result, which an agent would otherwise have to discover by calling.

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's terse 'Snapshot date, yyyy-MM-dd' by explaining that `date` scopes counts to that single day while omitting it covers stored history. It also clarifies that `page`/`page_size` affect retrieval only, not the facet aggregation — context the schema does not convey.

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 uses a specific verb ('Discover') with a concrete resource ('Steam player-count app and date values') and immediately characterises the output ('up to 50 ... facet buckets ordered by count'). Naming `datasets_steam_playercounts_search` as the tool with identical filters lets an agent place this among the facet siblings 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 gives clear operational context: the tool mirrors search filters, defaults to full stored history, and narrows to a single day when `date` is supplied. It does not state the classic facet use case explicitly ('call this first to learn valid app_id/snapshot_date values before searching') or name any when-not condition, so it falls just short of the top band.

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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