get_engagementEngagement rates for a contestant's profiles: current, baseline, and 30-day history (likes/comments-based).
| Name | Required | Description | Default |
|---|---|---|---|
| contestant | Yes | 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 description complements the readOnlyHint annotation by explaining what kind of data is returned: current, baseline, and 30-day history based on likes and comments. It does not describe internal side effects or edge cases, but the annotation already covers the read-only safety profile.
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 one concise sentence that leads with the result type, then defines the scopes and metric basis. Every phrase is informative and there is no redundant 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 single-parameter read-only tool with no output schema, the description adequately conveys the output dimensions: current, baseline, and 30-day history. It is not overly explicit about response formatting, but the tool is simple enough that this is unlikely to hinder correct invocation.
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 only parameter, contestant, is already documented in the schema with ID or slug examples and 100% coverage. The description adds little beyond restating that the engagement rates belong to a contestant's profiles, so it does not improve on 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?
The description clearly specifies what is returned: engagement rates for a contestant's profiles, including current, baseline, and 30-day history. It distinguishes from sibling tools by focusing on engagement rates rather than follower counts or events, but it does not explicitly contrast itself with those siblings.
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 for when to use this tool: when you need engagement rates for a contestant's profile. It does not explicitly state when not to use it or name alternative tools, but the scope is clear enough for a simple 1-parameter read-only lookup.
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.