Get TikTok live stream
tiktok_profile_live_checkCheck whether a TikTok creator is currently live by handle.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | TikTok handle to look up, with or without a leading @. |
tiktok_profile_live_checkCheck whether a TikTok creator is currently live by handle.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | TikTok handle to look up, with or without a leading @. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds no further behavioral context, such as response format or handling of non-existent handles, but it is fully consistent with the 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?
A single, tightly worded sentence with zero filler. The action and target are front-loaded, making the tool's purpose immediately apparent.
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 (one parameter, no output schema), the description is sufficient to understand its function and input. The return value is implicitly a boolean (live or not), which is acceptable for a check. No critical information 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 input schema fully documents the handle parameter with a helpful description ('TikTok handle to look up, with or without a leading @'). The tool description adds nothing beyond that, so with 100% schema coverage, 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?
States a specific verb 'check', a named resource 'TikTok creator', and the precise condition 'currently live'. This clearly distinguishes it from sibling tools like tiktok_profile_get or tiktok_profile_videos_list, which have different purposes.
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 the use case: when you need to know if a creator is live, this is the tool. It does not mention exclusions or alternatives explicitly, but the context is clear and unambiguous from the purpose statement.
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.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.