get_tiktok_profileFetch a current, normalized TikTok profile by username.
Returns identity, biography, verification, and audience metrics. A leading @ is accepted and usernames are normalized. This is a metered live-data request.
| Name | Required | Description | Default |
|---|---|---|---|
| username | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| ok | No | ||
| data | No | ||
| count | No | ||
| results | No | ||
| has_more | No | ||
| not_found | No | ||
| next_cursor | No | ||
| suggested_followups | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide read-only and non-destructive hints. The description adds useful behavioral context: 'metered live-data request', acceptance of a leading @, and username normalization. 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?
Three concise, well-structured sentences. Each sentence provides essential information: purpose/returns, normalization behavior, and metering. No fluff 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?
Given the tool's simplicity (1 parameter), existing output schema, and annotations that cover safety, the description covers all necessary aspects: what it does, input format, return types, normalization, and cost/metering caveat. Nothing essential 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 schema only defines 'username' as a string, but the description adds meaningful semantics: it accepts a leading @ and normalizes usernames. This exceeds the bare schema definition.
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?
Description states 'Fetch a current, normalized TikTok profile by username' with specific verb+resource+scope. It distinguishes from siblings by explicitly targeting TikTok profiles and enumerating returned data types (identity, biography, verification, audience metrics).
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?
Clear context is provided: this tool is for fetching TikTok profiles with normalization and live-data caveats. However, it does not explicitly state when not to use it or name alternative tools, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping boundaries: autocomplete_creators and search_creators are described as near-equivalent fuzzy lookups, get_creator and get_profile both resolve exact platform+username input, and the Instagram/TikTok post helpers overlap with generic get_posts. The descriptions work hard to disambiguate, but an agent would frequently need to choose between two or three equally plausible tools.
Most tools follow a clear verb_noun snake_case pattern, such as get_youtube_video, search_creators, match_creators, and render_creator_profile. Minor inconsistencies exist: singular/plural variants (get_instagram_post vs get_instagram_posts), list_ vs get_ for video listing, and search_creators carrying legacy semantic behavior under a lookup-sounding name.
Twenty-eight tools places the server in the 'too many' range, and the count is inflated by near-duplicates like autocomplete_creators/search_creators, get_creator/get_profile, and singular/batch transcript variants. Even with three social platforms and rendering helpers, the surface would be more focused around 18–22 tools.
The core influencer research workflow is well covered: handle resolution, batch lookup, semantic discovery, lookalikes, posts, transcripts, YouTube search, matching, and comparison rendering. Gaps are minor—there is no creator shortlist persistence or cross-platform comment support—but the main discovery-to-match path has no dead ends.