tiktok_feed_trending_listList currently trending TikTok posts for a region. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| trim | No | When true, requests a smaller response with fewer optional fields. | |
| region | Yes | Two-letter ISO 3166-1 country code for the request. |
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 a behavioral note about pagination: 'Returns a list (use cursor when paginated).' This goes beyond the annotations and helps the agent handle large result sets. No contradictions with annotations, and the addition is useful though not exhaustive (e.g., no mention of rate limits or result item structure).
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 sentences, front-loaded with the primary purpose and followed by a pagination note. There is no fluff or repetition, and every sentence earns its place.
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 no output schema, the description minimally states 'Returns a list' but does not describe the structure of list items or possible response fields. It also omits any mention of error conditions or rate limits. While the tool is relatively simple (2 params, 1 required), the lack of a return format leaves some ambiguity, making it slightly incomplete.
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%, with both parameters (region and trim) documented. The description itself does not add further meaning beyond what the schema provides; it only echoes 'for a region.' Since the schema carries the full burden, a baseline of 3 is appropriate.
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 operation: 'List currently trending TikTok posts for a region.' It specifies the verb (List), resource (currently trending TikTok posts), and a scoping qualifier (for a region) that matches the required region parameter. It is distinct from sibling trending tools by naming the platform (TikTok) and 'posts', setting it apart from Instagram or YouTube trending feeds.
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 provides context by stating 'currently trending' and 'for a region,' which implies this is for trending content filtered by geography. However, it does not explicitly mention alternatives or when not to use this tool. There is no guidance on distinguishing it from search or top-list tools like tiktok_search_top_list. The usage intent is clear but not explicitly contrasted with siblings.
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.