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datasets_steam_games_item

Retrieve a Steam game's stored dataset details by numeric appid: name, developers, genres, price, reviews, playtime, and Metacritic score. Missing appids return 404; no live scrape.

Instructions

Get a Steam game from the dataset. Returns one Steam game from the steam-games dataset by numeric appid: name, developers and publishers, genres and categories, price and discount, supported platforms, positive and negative review counts, owner estimates, average and median playtime, concurrent players, Metacritic score and language count. It reads the stored dataset and does not trigger a live scrape; an appid that is not in the dataset returns 404.

Input Schema

NameRequiredDescriptionDefault
appidYesSteam app id

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removedv1.6.0
  3. Addedv1.5.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden well: it states the data comes from a stored dataset, that no live scrape is triggered, and that a missing appid yields a 404. That covers read-only nature, data source, and error behavior, though it says nothing about auth or rate limits.

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?

Two sentences, front-loaded with the core action, then the return contents and behavioral caveats. Every clause earns its place; nothing is padded or repeated.

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?

There is no output schema, so the enumeration of returned fields (developers, price, review counts, playtime, Metacritic, etc.) is genuinely useful, and the 404/error case plus the 'stored dataset, no live scrape' note leave an agent fully equipped to call and interpret this tool.

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

Parameters3/5

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

Schema coverage is 100%, so the single `appid` parameter is already fully documented as an integer 'Steam app id'. The description adds only the word 'numeric', which is redundant with the schema's integer type – baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Get a Steam game') and immediately clarifies it returns a single record from the `steam-games` dataset keyed by appid. This clearly distinguishes it from the sibling `datasets_steam_games_search`, which finds games rather than fetching one.

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?

Usage is implied by 'by numeric appid' – an agent can infer this is the lookup to use once it has an appid. However, the description never explicitly states when to use this versus the search endpoint, nor does it name an alternative for name-based lookups, leaving the routing to inference.

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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