aggrometer

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

Because no annotations are provided, the description carries the full burden of behavioral disclosure. It does add useful context: the formula, same-hour per-game aggregation, the min 500 player threshold, and the >1.0 interpretation. But it omits output shape, ordering, pagination, and how the optional genre parameter changes behavior.

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?

The description is three sentences with no filler: first defines the metric, second explains what values mean, third gives a signal use case. It is front-loaded and efficient, though some interpretive prose could have been replaced with operational detail.

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 and no annotations, the description needs to explain what the tool returns and how the genre parameter works. It explains the aggro concept well but leaves out essential invocation and response details, making it incomplete for an agent deciding how to call it.

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?

Schema description coverage is 0% and the description never mentions the only parameter, 'genre'. The agent cannot infer whether genre filters results, groups them, or changes the metric calculation. This is a significant gap because the description must compensate for the undocumented schema but does not.

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 defines the aggro metric (live Twitch viewers / concurrent Steam players) and its interpretation, but it never states the tool's actual operation—no verb like 'list', 'compute', or 'return'. It tells the agent what aggro means, not explicitly what aggro_board does or returns.

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 description implies a use case ('A sudden aggro jump is an early attention signal') and notes watch-to-learn genres, which hints at when the tool might be relevant. However, it does not compare against siblings like hype_board, top_watched, or top_played, nor does it state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

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.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully reveals what the response contains (game counts, history hours, snapshot counts, freshness timestamps) and implies a read-only status operation. However, it does not explicitly state whether any data is modified, whether authentication is required, or whether this endpoint has any side effects—though the status-report framing makes those unlikely.

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?

The description is two sentences with no filler. The first sentence leads with the core purpose and immediately lists the concrete data points returned; the second adds a clear usage directive. Every word 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, no-output-schema status tool, the description is complete: it names the resource, enumerates the return content categories, and tells the agent when to invoke it. There is no missing operational detail that would prevent correct use.

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?

The tool accepts no parameters, so the schema is trivially exhaustive. The description adds useful context about what the returned summary will include, which is more than the empty schema provides. A baseline of 4 is appropriate for a zero-parameter tool.

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 states the tool's resource (warehouse status/data coverage) and enumerates the specific metrics it reports. It does not use an explicit verb like 'get' or 'retrieve', but the name and 'Call this first' make the operation obvious. It distinguishes itself from data-retrieval siblings by being the meta-level summary tool.

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 explicitly instructs the agent to call this first to understand data coverage, which is a clear usage directive. It does not mention alternative tools or exclusions, but the sibling tools are all specialized data views, making the summary tool's role evident from the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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

With no annotations, the description carries the behavioral disclosure burden. It covers the key behaviors: substring matching, catalog scope, and that the result includes both app ids and current player counts. It does not mention edge cases like no-results behavior, but the core operation is transparent.

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?

The description is two short, purposeful sentences with no filler. It front-loads the primary purpose and immediately follows with the return value and relationship to game_detail.

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 one-parameter search tool with no output schema, the description covers the input semantics and the essential return shape (app ids, player counts). It is sufficient for an agent to select and invoke the tool correctly, though it could mention what happens when no games match.

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?

Schema coverage is 0%, so the description must clarify the query parameter. It does this by stating that the query is matched as a substring against game names, adding real semantic meaning beyond the bare string type. Minor details like case sensitivity are not addressed, but the essential meaning is present.

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 states a specific action ('Find games'), a clear resource ('tracked catalog'), and matching behavior ('by name (substring match)'). It also differentiates from game_detail by noting this tool returns app ids for use with that sibling, so an agent can tell them apart.

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 description gives clear context: use this tool when you need to search the catalog by name and obtain app ids for game_detail. It does not explicitly enumerate when not to use it or name alternative search tools, but the intended workflow is clear enough for the catalog context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the result is a fixed list of 100 games, ordered by current concurrent players, updated hourly, and optionally scoped to one genre. It stops short of explicitly stating it is read-only, though that is strongly implied by the wording.

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?

The description is a single efficient sentence with no filler. It front-loads the core behavior ('Top 100 games by current Steam concurrent players'), adds freshness in a parenthetical, and folds the optional filter into the same sentence.

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 simple, one-optional-parameter, read-only ranking tool with no output schema, the description covers the essential context: what is returned, the ordering basis, update frequency, and optional genre filtering. It would be fully complete if it explicitly stated that omitting genre returns the global top 100, though 'optionally' makes that reasonably inferable.

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?

Schema description coverage is 100%, so the baseline is 3: the schema already documents the 'genre' parameter as an exact-match Steam genre name with examples and a pointer to genre_rollup. The tool description only restates the optional genre scoping and adds no new parameter-level meaning.

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 opens with the exact resource ('Top 100 games') and the ranking metric ('current Steam concurrent players'), which clearly states what the tool does. The 'updated hourly' qualifier adds temporal specificity, and the concurrent-players framing distinguishes it from sibling 'top_watched' without naming it.

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 description implies when to use the tool: to get a concurrent-player ranking, optionally filtered by genre. However, it does not explicitly name alternatives or state when not to use it, so the agent must infer the boundary with tools like top_watched or game_detail.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose useful traits: results are limited to 100, reflect current live Twitch viewership, update every 15 minutes, and can be genre-scoped. However, it does not describe the return structure, sort behavior beyond 'top,' or handling of invalid genre values.

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?

The entire description is one tight sentence that front-loads the core result ('Top 100 games') and then adds the cadence and optional filter. Every clause adds value and there is no filler or redundancy.

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 simple optional-parameter list tool, the description covers the essential context: what is returned, update frequency, and the one available filter. It is slightly incomplete by not describing the returned fields or what happens when no genre is supplied, and there is no output schema to fill that gap.

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?

The schema only provides the raw property name 'genre' with no textual description and 0% coverage. The description compensates meaningfully by stating that the parameter is optional, is a Steam genre, and scopes the result set to one genre. It still stops short of enumerating accepted genre values or format, but it is sufficient for basic invocation.

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 states a specific output: 'Top 100 games by current live Twitch viewership.' It also names the optional scope ('one Steam genre') and the 15-minute update cadence. This distinguishes it from likely siblings like top_played without needing to open schemas.

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 intended use is implied by the description: use this tool when the agent needs current Twitch viewership rankings, optionally filtered by genre. However, it does not explicitly contrast this with alternatives such as top_played or state when not to use it, so the guidance is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.