Creative Cult

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior. The description adds non-obvious behavioral details: ranking is by share of positive player reviews, a 500-review minimum applies, critic scores are ignored, and omitting month selects the latest ranked month. It does not describe return shape, but the output schema covers that.

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?

Four compact sentences, front-loaded with the ranking rule and threshold. Each sentence adds a distinct rule — ranking basis, minimum review count, critic-score exclusion, and default month — with 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?

For a one-parameter, read-only query with an output schema and rich annotations, the description supplies the essential ranking methodology, threshold, exclusions, and default. Nothing an agent needs to select or invoke it 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?

The single month parameter is fully described in the schema, including the YYYY-MM pattern and the ranked values available. The description adds the default behavior when month is omitted, which is not stated in the schema. With 100% schema coverage, this is a genuine semantic addition.

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 resource (a month's new video games) and ranking metric (share of player reviews that recommend them). It also gives the 500-review threshold and the critic-score exclusion. It does not explicitly distinguish the tool from siblings such as search_games or get_release_calendar, 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?

There is no explicit when-to-use or when-not-to-use guidance and no named alternatives. The default-to-latest-ranked-month note and the ranking threshold imply the intended browsing use case. This is adequate but leaves sibling selection to inference.

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, non-destructive, and closed-world, so the safety profile is fully covered. The description adds value by sketching the report's contents, but says nothing about behavior the agent might need, such as what happens when the game is ambiguous, not found, or has too few reviews. With annotations carrying the safety burden, a 3 is appropriate.

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 front-loaded sentence establishes what the report is, followed by a compact enumeration of its sections and a final clause on accepted input. Dense but every clause maps to actual report content; no filler or repetition.

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 single-parameter read tool with an output schema and full annotation coverage, the description is nearly complete: it tells the agent what the tool returns and where the input value comes from. It could be strengthened with edge-case behavior (ambiguous names, missing reviews), but nothing essential is absent.

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 is a single parameter with 100% schema coverage that already states it accepts the game name (e.g. "Marvel's Wolverine") or its slug. The description repeats this acceptance and adds the provenance hint (from search_games), which is mildly useful but does not add format or resolution semantics beyond the schema. 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 names a specific resource (one game) and enumerates the concrete content of the report: recommendation share and date, praise/criticism, fit, FAQ answers, and numbered sources. It also names the sibling search_games as the source of the input value, which helps distinguish it from discovery tools. It does not explicitly contrast itself with get_best_of_month or list_research, 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?

It clearly implies the workflow: obtain the game name or slug from search_games, then call this tool for one game's report. It gives context but no explicit exclusions or when-not-to-use guidance, such as what to do for multiple games or when a game has no reviews.

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, openWorldHint=false and destructiveHint=false, so safety is covered structurally. The description goes beyond them by disclosing the data provenance ('dates from publishers and platform holders'), that price is US-only and conditional ('where announced'), and that review data appears only 'for games already out'.

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 sentence, front-loaded with the primary purpose and with the default behavior placed at the end where it reads naturally. It is dense and slightly run-on in the middle clause, but every phrase carries information about the returned fields.

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 single optional-parameter read tool with full annotations and an output schema, the description covers scope, data sources, conditional fields, and default behavior. The only residual gap is the meaning of 'notable' — the curation rule behind which games appear — which is left unstated.

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 month pattern and the list of months with releases are already documented. The description nonetheless adds value the schema lacks by stating that the parameter defaults to the current month, which an agent needs to know before deciding whether to pass an argument at all.

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 a specific verb and resource, 'Notable video games releasing in a month,' and enumerates the payload (dates, platforms, US price, review recommendation share), so the agent knows exactly what comes back. It does not explicitly contrast itself with siblings like get_best_of_month or search_games, though the release-calendar framing is naturally distinct.

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 'Defaults to the current month,' which tells the agent the call works with no arguments, but there is no explicit when-to-use guidance nor any statement about when a sibling such as get_best_of_month or search_games would be the better choice.

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, non-destructive and openWorldHint=false, so the safety profile is covered without prose. The description usefully adds content scope — the agent learns this is a curated editorial corpus, not raw game data. It says nothing about result volume, ordering, or whether the list is exhaustive, which are the remaining behavioral questions an agent would have.

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 sentences, no filler: the first defines the corpus by naming its three content types, the second states the return fields. The identifying information is front-loaded and 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?

With no parameters, a full output schema, and rich annotations, the description carries little burden and meets it — the agent knows what domain the results come from and roughly what fields to expect. Only the scale and ordering of the returned list are unstated, which is a minor gap given the output schema exists.

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 takes zero parameters, so the baseline is 4. The description correctly implies the call is unfiltered — it simply returns the research index — and there is no parameter behavior left to explain.

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 the resource (Creative Cult's research pieces) and enumerates the specific content categories it covers — launch teardowns, the monthly Reception Index, and research-process write-ups — plus the fields returned. That is far more specific than the tautological title 'List research'. It stops short of explicitly distinguishing itself from the nearest sibling, get_best_of_month, which could plausibly return overlapping editorial 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?

There is no statement of when to call this versus alternatives such as get_best_of_month or search_games, and no exclusions. The agent must infer from the topic list alone that this is the entry point for Creative Cult editorial research rather than game data.

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 and non-destructive, so safety is covered. The description adds genuinely behavioral detail beyond that: the newest-first ordering, the empty-query listing mode, and the slug-as-prerequisite for deeper retrieval. Slightly redundant in restating return fields, which the output schema already carries.

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 sentences, front-loaded with the core purpose and then the sibling pointer, with zero filler. The enumeration of return fields is mildly wasteful given an output schema exists, but it is compact rather than rambling.

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 rich annotations, full schema coverage, and an output schema handling return values, the only remaining gaps an agent would face are ordering and empty-query behavior — both stated. Nothing needed 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 the baseline is 3; the description earns extra by defining the otherwise ambiguous empty-value case for query and the ordering applied to that result set. It doesn't add syntax rules for partial matches, but the schema's example ('wolverine') covers that.

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 (find video games) plus the scope that distinguishes it: games 'Creative Cult follows', searched by name. It also names the sibling get_game for full reports, so an agent can separate the lookup paths without opening either 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?

Explicit routing to the alternative: use get_game when a slug exists and a full report is wanted. It also specifies the degenerate case — empty query lists every game with a report, newest first — so the agent knows an empty call is legal and what it produces.

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