get_trend_eventsDetected follower surges and drops (trend events) with episode context — the headlines behind PopOff trend cards.
| Name | Required | Description | Default |
|---|---|---|---|
| show | No | Season ID or slug, e.g. "12" or "love-island-usa-season-8". Use list_shows to discover. | |
| status | No | Default: active. | |
| contestant | No | Contestant ID or slug, e.g. "394" or "amora-cachee". |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes that this is a safe read operation, so the description does not need to repeat that. The description adds some output context ('follower surges and drops', 'episode context'), but it does not disclose ordering, recency, pagination, or any computational caveats. There is no contradiction with annotations.
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 tightly worded sentence that front-loads the core behavior ('Detected follower surges and drops') and then adds the clarifying product context ('headlines behind PopOff trend cards'). No redundant or filler 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?
Given the simple optional-parameter read-only tool and no output schema, the description gives enough context about what is returned: detected trend events, episode context, and the pop-under behind the cards. It could mention return format or ordering, but the combination of description and schema is sufficient for an agent to select and call the 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 coverage is 100%, so each parameter is already documented with meaningful descriptions. The tool description adds no extra meaning about filters, status behavior, or contestant/show slugs. Baseline 3 is appropriate because the schema carries the parameter 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?
The description clearly identifies the resource as trend events and adds useful specificity: follower surges and drops with episode context, tied to PopOff trend cards. It is reasonably distinct from siblings like get_follow_events or get_follower_history, though it does not explicitly name a sibling or restate an imperative verb like 'get'.
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 usage context — PopOff trend card headlines — but never says when to prefer this tool over get_follow_events or get_follower_history. There are no explicit when-to-use or when-not-to-use instructions, leaving the agent to infer boundaries from the tool name and the phrase 'trend events'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct resource or analysis need: cast summaries, single-contestant details, follower time series, follow/unfollow events, follow graph, trend events, and export output are cleanly separated. The descriptions explicitly cross-reference related tools, so an agent should be able to pick the right one without ambiguity.
Tool names follow a consistent get_/noun and list_noun pattern, with export_season_csv as the only slight variation—but it still clearly uses verb_noun convention. camelCase is avoided, and duplicate or vague verbs are absent.
Twelve tools is a well-scoped size for a read-only analytics data API. Each tool contributes a meaningful slice of the domain—discovery, show details, cast metrics, raw series, events, graphs, trends, usage, and export—without redundancy or bloat.
The surface covers the full read-only workflow: discover shows and seasons, list episodes, inspect contestants and cast, retrieve follower histories, engagement, follow relationships, trend events, and export a citation-ready CSV. No obvious lifecycle dead ends exist since the API is inherently data-access-oriented rather than CRUD.