Aggrometer Anticipation Index: top-wishlisted unreleased Steam games scored with pre-release Twitch attention and Wikipedia attention surges (score = wishlist-rank points + log-scaled live viewers + log-scaled pageview surge vs 30-day average). A relative index - compare games against each other; week-over-week change is the launch-anticipation signal.
| Name | Required | Description | Default |
|---|---|---|---|
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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?
With no annotations, the description carries the full behavioral burden, and it does well: it discloses the exact scoring formula, log scaling, 30-day baseline, and relative interpretation. It does not mention output shape or update cadence, but for a zero-parameter read-only index that is a minor gap.
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 dense sentences with no filler: the first states what the tool is and how scores are computed, the second states how to interpret the index. Every clause earns its place.
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?
For a no-parameter tool with no output schema, the description explains the index meaning, scoring inputs, and the key week-over-week signal. It does not specify list size, sorting, or time window, but none of these are needed for a correct call.
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, so the parameter-semantics burden is nil. The description still adds meaning by explaining the scoring dimensions that determine the result, which is more than needed for a schema with an empty object.
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 clearly identifies the resource ('top-wishlisted unreleased Steam games') and the specific behavior (scoring them with Twitch and Wikipedia attention surges). The 'relative index' framing distinguishes it from siblings like top_played and top_watched without needing to open their definitions.
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?
It gives clear intended usage: compare games against each other and use week-over-week change as the launch-anticipation signal. It does not explicitly name alternatives or state when not to use it, so it stops just short of full 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.