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Glama

game_detail

Full profile of one game by Steam app id: hourly player history, Twitch viewer history, daily viewer-hours, price/discount history, top-seller rank appearances, clips-per-day virality, review counts and review-activity buckets back to launch, Wikipedia pageview history (monthly since 2018 + daily), developer news velocity, achievement-completion depth, and metadata (genres, developer, publisher). Use search_games first if you only know the name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_idYesSteam app id, e.g. 1623730 for Palworld

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does that well by listing the many returned data series and their time ranges, such as hourly player history, daily viewer-hours, and Wikipedia pageviews monthly since 2018 and daily. It does not mention rate limits, failure behavior, or exact response formatting, which prevents a 5.

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?

The key action and key identifier are front-loaded in 'Full profile of one game by Steam app id,' followed by a dense but organized enumeration of data categories. It could be slightly more scannable with bullets, but every phrase contributes useful information.

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?

Given the tool's complexity and the absence of an output schema, the description does a strong job of explaining what the returned profile contains and how to obtain the required app_id. Exact response structure and edge-case behavior are not covered, but the agent can select and invoke the tool correctly from this description.

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?

The schema already fully documents app_id with type, example value, and required status, so schema description coverage is 100%. The description adds no parameter-level detail beyond saying the profile is keyed by Steam app id, so the baseline 3 applies.

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 that this tool returns a full profile of one game identified by Steam app_id, then enumerates the specific data categories included. This differentiates it from search_games and get_summary in the sibling list, so an agent can immediately tell what it is for.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs the agent to 'Use search_games first if you only know the name,' which establishes when this tool should not be used directly and how to obtain the required app_id. This is concrete, actionable usage guidance tied to a named alternative.

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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TDQS

A3.7/5.0
Disambiguation5/5

Each tool targets a clearly distinct slice of the domain: per-game detail, genre aggregates, top lists, released-game aggro, unreleased-game hype, search, and warehouse status. Even the two 'board' tools are unambiguous because one is explicitly for live/released games and the other for unreleased anticipation.

Naming Consistency3/5

Tool names are all lowercase snake_case and generally readable, but they mix conventions: noun-style names like game_detail and genre_rollup sit alongside verb-style names like get_summary and search_games, plus adjective-style names like top_played and top_watched. The pattern is not chaotic, but it is inconsistent enough to be a minor usability issue.

Tool Count5/5

Eight tools is a well-scoped size for an analytics server covering catalog lookup, detailed game profiles, genre rollups, top lists, and specialized signal boards. Each tool earns its place and there is no obvious redundancy.

Completeness4/5

The surface covers the core workflows: understand data coverage, search for games, inspect a game, view rankings, see genre-level aggregates, and check both released-game and unreleased-game attention signals. A minor gap is the lack of a direct per-game aggro score endpoint, though the underlying data in game_detail makes this derivable.