Every Steam genre aggregated: game count, combined players, combined live viewers, genre-level aggro ratio, and combined hype (anticipation) of the genre's unreleased games. The whitespace signal: high players + low hype = an underserved genre; low players + high hype = a wave arriving.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral burden. It discloses the full scope (all genres, aggregated) and the metric set, including clarifying that hype means anticipation from unreleased games. It stops short of describing output shape or sort order, but for a no-parameter tool this is a solid contract.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two punchy sentences: the first defines what the tool returns, the second gives an interpretive heuristic. No filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description covers the key return fields and gives a use case, which is enough for an agent to invoke a zero-parameter tool. Minor gaps like the definition of 'aggro ratio' and output ordering keep it from being fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters and the schema only declares an empty object, so there is nothing for the description to add. The baseline of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource ('Every Steam genre') and an aggregation operation, then enumerates the exact metrics returned. It is clearly distinct from sibling detail/search/top tools even without naming them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The whitespace-signal sentence gives explicit guidance on when the output is useful: high players + low hype vs low players + high hype. It does not name sibling alternatives or exclusions, but for a zero-parameter rollup this is meaningful contextual guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.