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datasets_steam_reviews_search

Search Steam reviews by full text and filter by app ID, language, or positive/negative rating; sort results by helpfulness, weighted score, or date.

Instructions

Search the steam-reviews dataset. Searches the stored Steam review corpus (the most-helpful reviews per game; one document per appid × recommendation). Full-text q over the review body, filter by app_id, language, or voted_up (positive/negative). Sort enum: votes_desc (most-helpful first, default), weighted_desc, date_desc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over the review body, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: votes_desc, weighted_desc, date_desc
app_idNoExact Steam app id filter
languageNoReview language filter (e.g. english, schinese)
voted_upNoRecommendation filter: true (positive) or false (negative)
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "votes_desc",
+  "weighted_desc",
+  "date_desc"
+]
  2. Addedv1.5.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It adds useful context about the corpus composition and clarifies the meaning of the sort enum (e.g., votes_desc is 'most-helpful first' and is the default). However, it does not disclose pagination behavior, maximum result counts, or any potential side effects, which are relevant for a search tool but not critical.

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?

The description is three sentences long, front-loaded with the core purpose, and each sentence adds distinct value: corpus definition, filter capabilities, and sort semantics. There is no redundancy or fluff, making it easy for an agent to parse quickly.

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 search tool with 7 optional parameters and no output schema, the description covers the essential aspects: what data is searched, available filters, and sort behavior. It lacks details on response format, pagination limits, or how filters combine, but these are not critical for correct invocation. The description is sufficient for an agent to call the tool appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds significant semantic value beyond the schema: it explains the `q` parameter as full-text over the review body, defines `voted_up` as positive/negative, and elaborates on the sort enum meanings and default (votes_desc). This goes beyond the schema's terse descriptions, providing an agent with actionable insight for parameter selection.

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 clearly states the tool's purpose: to search the steam-reviews dataset, specifying the corpus content (most-helpful reviews per game, one document per appid × recommendation). It distinguishes itself from sibling tools by targeting reviews specifically, unlike datasets_steam_games_search or datasets_steam_achievements_search, so an agent can readily identify the correct tool.

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 description implicitly conveys when to use this tool—when searching Steam review data—and explicitly lists the available filters and sort options, giving clear operational context. However, it does not explicitly state when not to use this tool or mention alternative tools for other Steam datasets, though the scope is obvious from the dataset name.

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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