Wieldr game radar

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

Annotations only declare it as a safe, idempotent read; the description supplies the real caveats: heat weights are an uncalibrated estimate, revenue figures are ranged estimates and never measured sales, and computed niches return null for stage/heat/window/studios. Grade semantics (A/B/C/D data-point completeness) give the agent a way to judge reliability before acting.

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?

It is front-loaded with the mode distinction, but the body is one long comma-chained sentence that enumerates output fields and buries the grade and revenue caveats mid-stream. Much of the field enumeration duplicates the output schema.

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?

Because an output schema exists, the field-by-field enumeration is largely redundant, while the genuine gaps (when to pick this over compare_games or find_opportunities, and whether platform omission changes the mode) remain unaddressed. It is nonetheless complete enough on methodology and data reliability to call the tool correctly.

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 already 100%, so the baseline is 3; the description earns more by explaining that a niche can be a ranked label, a genre, or a raw keyword, and that crossPlatform maps the same mechanic between Roblox and Fortnite. It adds resolution semantics the schema's examples do not fully convey.

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?

States a specific verb and resource ('Evaluate one niche') and immediately explains the two modes a niche can be resolved into ('tracked' vs 'computed'). It never names a sibling, so an agent comparing it against get_gap_analysis or find_opportunities must infer the split itself.

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?

Usage is implied ('always in the same format') and the niche parameter is described as coming 'from list_niches', which gives a weak ordering hint. There is no explicit when-to-use-this vs when-to-use-a-sibling guidance, no prerequisites, and no exclusions.

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?

Annotations already declare readOnly/idempotent/non-destructive, and the description adds substantial context beyond them: asOf timestamps, source attribution, grade semantics, and explicit caveats that revenue is an estimate range, never measured sales. It also warns which metrics are cross-platform comparable. It stops short of describing pagination or response shape, but the output schema covers returns.

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?

Front-loaded with the core purpose, then caveats and definitions. Dense but each clause carries information; the grade-definition parenthetical is long but genuinely useful. Slightly heavy for a one-parameter tool.

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 read-only comparison tool with a full output schema, the description covers scope, comparability caveats, data provenance, and confidence grading. An agent has everything needed to call it and interpret results correctly.

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 100% and the schema already documents accepted id formats and the 2-4 bound, so the description adds little new parameter meaning. Baseline 3 applies 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.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('side-by-side records of 2 to 4 games') and enumerates the compared fields (players, momentum, reviews, age, revenue, complaints). The reference to 'same fields as get_game' implicitly distinguishes it from the single-game sibling.

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 2-to-4 game scope implies when to use it, but there is no explicit when-to-use/when-not guidance and no named alternative (e.g., get_game for a single title, analyze_niche for market-level comparison). Usage is inferable rather than stated.

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?

Annotations already declare readOnly/idempotent/non-destructive, yet the description still adds substantial non-obvious behavior: a default floor hiding small Steam games (50+ reviews in 7 days and 50+ players online), the exclusion of games with unknown positive share in minPositivePct results, and source/asOf stamps on every record. It stops short of describing pagination limits beyond nextCursor or result ordering ties, so it is strong but not exhaustive.

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?

Purpose and platform caveats are front-loaded, and most sentences carry operational detail rather than filler. The grade definition is dense and nested inside parentheses ("grade = completeness of the game record (A = 8+ of 10 data points known, B = 6-7, C = 4-5, D = fewer)"), which is harder to parse than a short pointer would be, but no sentence is truly wasted.

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 14-parameter filtered-list tool with an output schema and full read-only annotations, the description covers what annotations and schema cannot: cross-tool routing, default hiding behavior, definitional resources, and the estimate-grade caveat on revenue figures. Nothing an agent needs to call it correctly or interpret results is missing.

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 100%, so the baseline is 3, but the description adds real meaning beyond the schema: sort values denote the field sorted by, momentum is the default, newest means smallest ageDays, and it explains why includeSmall exists by describing the hidden floor. The grade/estimate caveat clarifies how to read returned figures rather than adding new filter semantics.

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?

Opens with a specific verb and resource plus scope: "Find games that are gaining players or reviews right now," then immediately names the covered platforms and the ones lacking momentum data. It explicitly positions itself relative to siblings ("Start here for what is rising questions, then call get_game on an id"), so an agent can choose it without inspecting another schema.

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?

Gives explicit when-to-use guidance ("Start here for what is rising questions") and the follow-up step (get_game on an id), plus a pointer to list_niches for nicheStage values and the wieldr://methodology resource for definitions. It also names the condition that changes behavior (includeSmall for hidden small Steam games), which is the key usage decision an agent must make.

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?

Annotations only declare read-only/idempotent/closed-world; the description goes well beyond that by enumerating the exact fields returned (stage, window, heat, timing verdict, feasibility level, barrier thresholds, one-sentence reason) and by flagging that these are documented heuristics, not scores or forecasts. It also discloses data provenance (asOf in UTC, source, letter grade with the A/B/C/D thresholds and estimate-range meaning) and a revenue-estimate caveat — precisely the caveats an agent would otherwise get wrong.

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?

It is front-loaded with the strongest hook and stays information-dense with no filler sentences. It is, however, a long run-on chain of parentheticals, and the embedded grade-definition block sits awkwardly inline next to a pointer to wieldr://methodology where it arguably belongs.

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 five-parameter, read-only ranking tool with an output schema, this covers what an agent needs to call it correctly and to interpret grades and estimate ranges honestly, and it names the natural next step. The remaining gap is sibling positioning — nothing tells the agent why to choose this over list_niches or analyze_niche for a given question.

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 description adds only modest meaning beyond the schema: it clarifies that ranked niches exist on Roblox and Fortnite (reinforcing the platform enum) and that team size and horizon drive ranking, but the more useful nuance (e.g. that horizon only moves closing/crowded niches) already lives in the schema text.

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 opening clause states a specific verb and resource ("ranks every ranked niche (Roblox and Fortnite)") plus the two ranking axes (team size, build horizon), which is far more than a restatement of the name. It implicitly separates itself from a plain listing like list_niches, but never names a sibling it is *not*, so disambiguation from analyze_niche relies on inference.

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 framing question ("What to build, with what odds, and whether it is too late") gives a clear decision context, and it prescribes a concrete follow-up ("Then call get_gap_analysis on a key"). It does not state when to prefer list_niches or analyze_niche instead, so the routing guidance stops short of explicit alternatives.

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?

Annotations already declare a safe, idempotent, closed-world read, so the bar is lower, and the description still contributes real behavioral context: every record carries asOf (UTC export time), source and a completeness grade with a defined A/B/C/D rubric, and revenue is explicitly a range-based estimate with a grade, never measured sales. The pointer to wieldr://methodology for definitions is a useful escape hatch. Minor gap: nothing about pagination or what happens when a game is not found.

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 body is essentially one very long run-on sentence enumerating a dozen-plus return fields — content that an output schema already carries, since one exists. Because the enumeration is buried mid-sentence, the genuinely load-bearing parts (estimate caveat, grade rubric) are hard to find. It would be far stronger as three short lines: purpose, input resolution, estimate disclaimer.

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 two-parameter read tool with full schema coverage, annotations and an output schema, an agent has everything needed to call it correctly, and the estimate-grade caveats plus the asOf/source framing give the right epistemic posture toward the numbers. The only shortfall is on the selection side, where the sibling landscape is left unaddressed.

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% and both parameters are documented in detail there, including the id format examples and the platform enum, so the baseline applies. The description reinforces accepted inputs (id, Fortnite island code, title) but adds no semantics the schema lacks, and its field list describes outputs rather than inputs.

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?

"Full record of one game" states a specific verb-and-resource pair, and the enumeration of covered platforms and data points (players online, revenue range, robloxStructure, fortnite) makes the scope unambiguous. It stops short of naming a sibling — nothing distinguishes it explicitly from get_game_history or compare_games — so an agent must infer that this is the single-game snapshot and those are the time-series/comparison variants.

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?

Usage is implied by the resource shape (one game, full record) rather than stated; there is no "use this when… use X instead when…" routing despite eleven siblings including a near-neighbor in get_game_history. The one genuinely useful usage hint is about input resolution — a title resolves to the most played match with other matches listed — which tells the agent how to disambiguate a fuzzy input but not when to pick 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?

Annotations already declare read-only/idempotent safety, so the bar is low, but the description still adds real behavioral detail: the reading cadence (hourly vs daily), the approximate lookback (~48 readings), the point encoding '[UTC time, value]', and critically that a gap means missing data rather than zero. That last note prevents a common misinterpretation and is exactly the kind of context structured fields cannot convey.

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?

Three tight sentences, front-loaded with what is returned, then when to use it, then how to read the payload. Every clause carries information: series types, cadence, use case, and the gap-vs-zero caveat; nothing is padding.

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?

Although an output schema exists, the description still supplies the two things an agent needs to interpret it correctly: sampling granularity and the gap semantics. Combined with complete parameter docs and non-destructive annotations, nothing material is missing for a read-only history tool.

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 game, platform enum, and maxPoints bounds are already fully documented, which sets the baseline at 3. The description mentions '~48 readings' and 'most recent kept' implicitly but adds no format or fallback detail beyond the schema, so it neither compensates for nor undercuts the schema.

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?

Names the concrete resource (one game) and the exact series returned: hourly players online, daily positive/negative Steam reviews, cumulative visits/followers/reviews, and Wikipedia page views. The opening 'Time series' framing contrasts implicitly with the point-in-time sibling get_game, so an agent can distinguish them without opening 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?

'Use it to tell a lasting rise from a one-day spike' gives a clear positive trigger for when this tool is the right choice. It stops short of naming an alternative (e.g. get_game for a current snapshot) or stating when not to use it, so it is clear context but not full routing 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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the description rightly spends its budget elsewhere: it discloses that null and measured=false mean no data rather than zero, that every game carries asOf/source/grade, that estimates are ranges rather than measured sales, and that a dataGaps list is returned. These are genuine interpretive caveats an agent could not infer from structured fields. It does not cover freshness limits, cost, or known coverage gaps by platform, which keeps it off 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 lead sentence front-loads the payload well, but it is a single sprawling sentence with four levels of nested parentheses, including a grade definition embedded in a parenthetical inside another parenthetical. Every element carries information, yet the density and punctuation load make it slower to parse than warranted; splitting the grade/A-B-C-D block and the estimate caveat into separate short sentences would improve it without losing content.

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?

An output schema exists, so return shape need not be explained, and the description instead handles the parts structure cannot: null vs zero, estimate ranges, grade meaning, and the dataGaps disclosure. Coverage gaps are minor, mainly that the platform parameter and the top-5 ranking basis go unmentioned. For a read-only niche-analysis tool, this is close to 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?

Schema description coverage is 100%, so both parameters (niche, platform enum) are fully documented structurally and the baseline of 3 applies. The description adds no parameter-level meaning beyond noting that niches are "ranked" and that games are the top 5, and it never mentions platform or the omit-for-both behavior.

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?

"What the top 5 games of a ranked niche lack" plus the explicit inventory (complaint themes, requests, structure, retention, dataGaps) makes the resource and its scope unambiguous, and the phrase "pick an angle for an original game" frames the intent. It is not a clean verb+resource statement and never explicitly contrasts itself with close siblings like analyze_niche or get_player_complaints, so it stops short of a 5.

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?

Usage is only implied through the single clause "Use it to pick an angle for an original game," which does signal the scenario but is easy to miss mid-paragraph. There is no when-not guidance, no exclusion of overlapping tools (analyze_niche, get_player_complaints, find_opportunities), and no stated prerequisite beyond the niche label being sourced from list_niches or find_opportunities.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is fully covered upstream. The description adds genuinely useful behavior context: it discloses dataset freshness signals and that revenue methodology plus disclaimer are included, telling the agent the response carries caveats. It says nothing about response size, pagination, or latency for what is presumably a large aggregate payload.

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?

One ~70-word run-on sentence enumerating a dozen return fields, with the highest-value instruction ("call it first") buried at the very end. The lead phrase "Platform-wide picture and dataset freshness" is vague and the middle is a comma-spliced inventory that would read far better as a structured list or a trimmed summary.

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?

There is an output schema, so the return values need not be spelled out, and with one optional enum parameter the input side is fully covered. The description is complete enough for correct invocation; the long return inventory is redundant rather than missing, and no operational caveats (payload size, caching) are absent that would block a 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?

Schema description coverage is 100% and the single enum parameter is fully documented, so the baseline is 3. The description's parenthetical "(Fortnite included)" marginally reinforces platform coverage but adds no new scoping semantics beyond the schema's "omit for all platforms".

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 makes clear this is a broad orientation/statistics tool rather than a targeted query, and the breadth of listed outputs (platform counts, momentum coverage, anomalies, medians, top genres, Steam reviews, Roblox/Fortnite niches, methodology) distinguishes it well from siblings like get_game or analyze_niche. It leans toward an inventory of return contents instead of a crisp verb+resource statement, which keeps it from a 5.

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?

"Call it first to know what the data covers" gives explicit sequencing guidance that no sibling tool claims, so an agent knows this is the entry point. It stops short of naming when-not-to-use it or pointing to specific alternatives for drill-down, which is what would earn a 5.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds crucial data-quality caveats beyond them: measured=false means absence of data rather than zero, provenance differs by platform (Steam sampled negative reviews, Roblox YouTube comments), and Fortnite coverage does not exist yet. Those are exactly the behavioral facts an agent needs before interpreting results.

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?

Very dense but front-loaded: output shape comes first, then scope, then data caveats, then usage. The opening two clauses are packed into a single comma-spliced sentence, which slightly impedes scanning, but no sentence is 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?

Covers scope, return fields, sample-size semantics, per-platform source coverage, and intended use, while an output schema exists so return values need no further explanation. Nothing material for a correct call is missing.

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 100%, so the baseline is 3, and the description still adds meaning: it explains that game, niche, and the implicit no-argument case ("all moving games") are alternative selectors rather than combinable filters. It does not address limit or cursor behavior, but those are fully documented in the schema.

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?

States the specific verb and resource (complaint themes with sharePct and sampleSize) and the three scoping modes (game, niche, all moving games). It is instantly distinguishable from siblings like get_game or analyze_niche, which return different data shapes.

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?

Gives a clear intended use ("Use the themes as angles for an original game that fixes them") and points to list_niches as the source for niche labels. It does not explicitly name when to prefer a sibling such as analyze_niche or get_gap_analysis instead, so routing is inferred rather than stated.

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?

Annotations already establish readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: the exact 6- and 12-month complete windows, the scaling convention for shareChange6m (1.25 = +25%), the default sort, and the caveat that playtime is not collected and returns null. It stops short of describing pagination limits or the empty-result case.

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 core purpose and metric list are front-loaded in the first sentence, with filtering/sorting and scope caveats following. It is dense but every clause carries information; the only mild inefficiency is the long comma-separated metric inventory, which reads as a list rather than prose.

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 an output schema present, the description need not explain return values, and it correctly focuses on scope, windows, metric definitions and the playtime caveat. Combined with 100% schema coverage and full annotations, an agent has enough to call it correctly. Minor gaps remain around the unit of median price and pagination termination, but they are not blocking.

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 schema already documents tag, sort, limit, order and cursor, giving a baseline of 3. The description adds interpretation for the sort field values (shareChange6m semantics, default sort) but does not explain the other sort fields, the order default, or the limit cap beyond what the schema states.

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?

States a specific verb and resource: tag-level Steam genre trends over the last 6 and 12 complete months, and enumerates the returned metrics (shareChange6m, releases, median reviews, 100+ review share, median price, free share, releases by month). An agent can tell this is a tag-aggregate trend tool rather than a per-game tool like get_game or compare_games. It never names a sibling, so the differentiation is inferred from the resource rather than stated.

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?

Offers scope hints — 'For indie projects of 6 to 24 months' and a default sort — but gives no explicit when-to-use versus when-not guidance. It does not say how this differs from analyze_niche, get_market_overview, or find_opportunities, which overlap in the trend/market space. Usage is implied rather than directed.

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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real context beyond them: the corpus is limited to public videos already linked to rising games and only the hottest anomalies, results are view-sorted, and paging is via nextCursor. It does not discuss rate limits, staleness, or linking latency.

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 core scope and returned fields are front-loaded in the first clause, followed by filters, paging, and purpose in compact order. It is dense but every sentence carries information; the only minor cost is that the returned-field list makes the opening sentence long.

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?

An output schema exists, so return-format detail is unnecessary; the description instead covers what matters for invocation: source platforms, the rising-games linkage, view-based sorting, filter dimensions, pagination, and the corpus limitation. Nothing an agent needs to call this correctly is missing.

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 100%, so every parameter including the enum and cursor is fully documented in the schema. The description only restates the filter surface (game, title keyword, platform) and the paging mechanism, adding little meaning beyond what the schema already provides; notably 'title keyword' is not explicitly tied to the `niche` parameter name.

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?

States a specific verb+resource: public TikTok and YouTube videos already linked to rising games, sorted by views, with the returned fields (url, network, views, publish date, game) named explicitly. The scope is narrow and distinct from every sibling tool, all of which operate on game/market analytics rather than video content.

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?

Gives a concrete use case ('use them to study hooks') and a scoping constraint ('videos are tracked for the hottest anomalies only'), which tells the agent what will and will not be present. It stops short of naming an alternative sibling, but no sibling overlaps this retrieval, so no exclusion is needed.

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?

Annotations already establish readOnly/idempotent/non-destructive, and the description adds substantial context beyond that: every game carries asOf, source and a completeness grade, revenue figures are explicitly estimates with a range and are "never measured sales and never a guarantee," and stage can be null with a stageNote explaining why. That is exactly the kind of trust and data-quality framing an agent needs before quoting numbers.

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 name, scope and default ordering are front-loaded, which is good, but the body is one dense block with heavily nested parentheticals, especially the grade definitions (A/B/C/D and estimate grades) that are hard to parse inline. The trailing "Definitions: resource wieldr://methodology" is unelaborated and reads as an afterthought. Structuring the field list and grade table would cut reading cost materially.

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?

An output schema exists, so return shape need not be spelled out, and the description still covers data provenance, estimate caveats, paging and downstream calls. The remaining gap is the platform default ambiguity, which is a correctness-relevant omission rather than a cosmetic one.

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 100%, so the baseline is 3, and the description does add meaning for sort fields (heat, new30Winners, top1Share, median 24h growth) and the null-stage caveat. However it states the default platform is Roblox, while the schema says omitting platform returns all platforms — an agent could reasonably call it with no args expecting Roblox-only and get everything. That conflict undercuts the added value.

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?

Opens with a specific verb and resource ("Ranked niches of Roblox (default) or Fortnite"), states the ordering (hottest first) and enumerates the exact returned fields, so an agent knows precisely what it gets back. It is clearly distinguishable from siblings like find_emerging_games or get_market_overview, which are aggregation/quality tools rather than ranked niche listings.

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?

Gives concrete next-step routing: "Then call find_opportunities, analyze_niche or get_gap_analysis on a label," which tells the agent this is a discovery entry point in a chain. It also describes filtering and paging mechanics. It does not say when to prefer this over compare_games, get_tag_trends or find_emerging_games, so the exclusion side is missing.

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?

Annotations already declare readOnlyHint, idempotentHint and non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that: nulls carry a reason and daysMissing, every record has asOf/source/grade, and estimates are explicitly not measured sales. Most of this, however, restates the return shape that the output schema already defines.

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 core purpose is front-loaded in a single sentence, but that sentence is a dense run-on with nested parentheses, and the grade definition plus the revenue-estimate caveat are stacked inline rather than separated. It is information-dense but harder to parse than a short purpose statement followed by structured notes.

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 an output schema and fully documented parameters, the description adequately covers data provenance, nullability semantics and estimation caveats. Its remaining gap is competitive positioning against the many sibling tools, which is not addressed at all.

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%, with the game parameter documenting id formats, title matching and tie-breaking, and platform enumerating accepted values. The description adds nothing about either parameter — platform is not mentioned at all — so this sits at the baseline the schema earns on its own.

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 concrete resource and scope: the trajectory of one game benchmarked against the medians of its up-to-3 largest niches, with specific metrics (24h growth, momentum, D7/D30 retention). It is specific enough for an agent to know what comes back, but it never contrasts itself with close siblings such as get_game, get_game_history or analyze_niche.

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?

There is no explicit when-to-use guidance, no when-not-to-use condition, and no named alternative among the twelve siblings. The only routing signal is implicit in the word 'trajectory', leaving the agent to infer that this is the single-game benchmarking call versus the niche-level or history calls.

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