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datasets_steam_news_facets

Discover Steam news app IDs by grouping matching articles into up to 50 count-ordered buckets, using the same full-text query and app_id filter as the related search tool.

Instructions

Discover Steam news app ids. Returns up to 50 Steam news app-id buckets ordered by count, using the same full-text query and app_id filter as datasets_steam_news_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_news_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Full-text news-title/body 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
`page_size`	No	Page size, default 20 and max 100

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description must carry the behavioral load, and it does disclose the key quirks: a hard cap of ~50 buckets, count-desc ordering, and that page/page_size are inert for the aggregation. However, it omits whether the call requires auth, what a bucket record contains (e.g., a count field), and what happens when the query matches nothing.

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 dense, front-loaded sentences with no filler — purpose first, then return shape, then parameter caveats. Slightly compressed phrasing ('Page and page_size do not change the aggregation' lacks the 'but do affect the underlying search' half), but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no annotations, so the description is the only spec. It covers the bucket count, ordering, and aggregation invariance, but does not describe the shape of a bucket (id plus count?), facet-value validation, or empty-result behavior, leaving gaps for a tool whose return structure is otherwise undocumented.

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 baseline is 3, but the description adds genuine semantics beyond the schema: `page`/`page_size` do not affect aggregation, `sort` does not reorder buckets (they stay count-desc), and time-series date_desc retains its search-history scope. Those are non-obvious interactions the schema text alone does not state.

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 Steam news app ids') and immediately pins down the return shape: up to 50 app-id buckets ordered by count. It explicitly distinguishes itself from the sibling `datasets_steam_news_search` by noting it reuses the same query/filter surface but aggregates instead of listing.

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?

The reuse of the same full-text query and app_id filter as the search sibling implies a clear discovery/refinement workflow, and the note that page/page_size do not change the aggregation steers agents away from pointless pagination. It stops short of an explicit 'use this when…, use search when…' sentence, so it is context rather than full routing guidance.

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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