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by net.crawlora
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datasets_steam_reviews_facets

Read-only

Return up to 50 facet buckets with counts for one Steam review field using the filters from datasets_steam_reviews_search. facet accepts app_id, language, or voted_up; page and page_size do not change counts; sort is validated but does not change bucket order. Use datasets_steam_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_steam_reviews_facets"
  }
}

Input Schema

NameRequiredDescriptionDefault
qNoOptional full-text query over the review body, max 256 characters.
pageNoResult page number, 1-based, default 1; page times page_size must not exceed 10000.
sortNoOptional sort order. Allowed values: votes_desc (most-helpful first, default), weighted_desc, date_desc.
facetYesrequired,Required facet. Allowed values: app_id, language, voted_up.
app_idNoOptional exact Steam app id filter.
languageNoOptional review language filter (e.g. english, schinese).
voted_upNoOptional recommendation filter: true (positive) or false (negative).
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

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations it discloses three non-obvious behaviors: the hard 50-bucket cap, that page/page_size are no-ops for counts, and that sort is validated but does not reorder buckets. These are exactly the quirks that would otherwise cause an agent to make redundant calls or misread results.

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?

Three sentences, all load-bearing: output shape first, then parameter quirks, then sibling routing. No restatement of the name or title, and the most decision-relevant facts are front-loaded.

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 covers the return shape and annotations cover safety, so the description only needs to cover aggregation semantics, filtering inheritance and the cap — all of which it does. Nothing needed to call the tool correctly 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, but the description adds real meaning: facet operates on exactly one field at a time, the three legal field names, and the no-effect semantics of page, page_size and sort that the schema does not state. It also clarifies that the remaining filters are shared with the search tool.

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 opens with a specific verb, resource and output shape: 'Return up to 50 facet buckets with counts for one Steam review field'. It explicitly scopes the input to filters from datasets_steam_reviews_search and distinguishes itself from that sibling, so an agent can separate aggregation from record retrieval without opening either 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 names the alternative ('Use datasets_steam_reviews_search to find review records') and implicitly routes counting/faceting here. It also warns that page/page_size do not change counts, which steers the agent away from pointless pagination. The only gap is the absence of an explicit statement of when a facet query is the wrong choice.

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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