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?
Beyond annotations (readOnlyHint, openWorldHint), the description discloses that data is 'current' and 'normalized', that a leading @ is accepted, and that it is a 'metered live-data request'—important cost/latency context. It also lists returned categories, adding behavioral expectations without contradicting 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 sentences: purpose, return contents, and input handling/cost context. Each sentence is informative and front-loaded with the core action. No filler or redundancy.
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 (not shown), the description does not need to detail return structure. It covers key aspects: input normalization, live/metered nature, and high-level output categories. For a simple one-parameter tool, this is complete and appropriately scoped.
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 has one parameter (username) with no description, so the description carries the full burden. It explains that a leading @ is accepted and usernames are normalized, giving practical meaning to the parameter. While it doesn't provide examples or edge cases, it sufficiently compensates for the 0% schema coverage.
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 uses specific verb 'Fetch' with resource 'TikTok profile' by username, clearly distinguishing from general profile/creator tools. It also states the scope (current, normalized) and returns high-level metrics, making the tool's purpose unambiguous.
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 indicates this is for TikTok profiles and is a live-data request, implying when to use it (when you need current TikTok data) but does not explicitly name alternatives or exclusions. It provides clear context but lacks direct comparison to sibling tools like get_creator or get_profile.
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.
Multiple tools have unclear boundaries: `autocomplete_creators` and `search_creators` both claim the exact same example query ('Who is that fitness coach called Jane?') as their primary use case, creating direct routing conflicts. `get_creator` and `get_profile` also overlap heavily for exact platform+username lookups, with descriptions admitting the choice depends on whether 'profile metrics are the main need' — a thin distinction. `search_creators` further muddies things by dual-routing to legacy semantic search, making it a hybrid that competes with both `autocomplete_creators` and `semantic_search_creators`.
The naming follows a mostly consistent verb_noun snake_case pattern: `get_*` covers record fetching, with clear singular/batch pairs like `get_instagram_post`/`get_instagram_posts` and transcript variants. Minor deviations exist (`semantic_search_creators` prefixes a modifier, and `autocomplete_`, `find_`, `match_`, `lookup_`, `render_` each introduce different verbs), but the style is uniform and the verb typically reflects the operation type.
At 28 tools the server is heavy, but the scope is genuinely broad — three platform-specific data surfaces (Instagram, TikTok, YouTube), each requiring profile/video/transcript/listing operations, plus creator search, matching, and rendering. The count is inflated by redundancy, though: four `render_*` tools that could collapse into one parameterized tool, and batch variants of the Instagram raw-data endpoints. It is borderline acceptable for the platform-multiplied domain rather than chaotic bloat.
The tool surface covers the full read-only creator workflow: fuzzy lookup (autocomplete/search), exact profile fetch (get_profile/lookup_profiles), discovery (semantic_search/find_lookalike), fit scoring (match_creators), content evidence (get_posts), and presentation (render_*). Notable gaps include no Instagram-specific profile endpoint (odd given TikTok/YouTube have dedicated ones), no YouTube comments, and no audience-demographic data, but agents can complete realistic workflows without dead ends.