tiktok_users_searchSearch TikTok users by keyword. Discover real handles before profile gets; avoid inventing usernames.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query text for TikTok users. | |
| cursor | No | Opaque pagination cursor returned by a previous response. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations mark this as readOnlyHint=true and openWorldHint=true. The description adds behavioral context beyond annotations: it states the tool returns real handles and warns against inventing usernames, which helps the agent understand the expected outcome and use it appropriately. No contradictions 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?
The description is two concise sentences. The core action is stated first, and the additional guidance is brief and valuable. No filler or redundant information.
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?
The description does not mention pagination behavior or response format, but there is no output schema. It does cover the essential purpose and provides a usage hint. Given the tool's simplicity and the read-only annotation, it is adequate but could benefit from a note about the return structure or pagination.
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 description coverage is 100%—both 'query' and 'cursor' have descriptions in the schema. The tool description adds no parameter-specific information beyond what the schema provides, so the baseline of 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?
The description clearly states the action: 'Search TikTok users by keyword.' It specifies the resource (TikTok users) and distinguishes it from other TikTok search tools (e.g., tiktok_search_videos_list) by targeting users specifically. The additional phrase 'Discover real handles' reinforces the purpose.
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 when to use this tool: when you need to find real handles before profile retrieval, and to avoid inventing usernames. It does not explicitly name alternative tools, but within the context of many TikTok search tools, this guidance is clear enough for an agent to choose it over others.
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.