tiktok_search_suggestions_listGet TikTok search suggestions by query. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query text for TikTok suggestions. | |
| region | No | Optional two-letter ISO 3166-1 country code for localized suggestions. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds pagination behavior ('use cursor when paginated'), which is useful but minimal. It does not elaborate on result limits, ordering, or error conditions, but given the simple list nature and existing annotations, this is adequate.
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?
Two succinct sentences with zero waste. The core purpose is front-loaded, and the pagination note is appended efficiently. Every word 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?
For a simple suggestions-list tool with a read-only annotation and full schema, the description covers the essential purpose and pagination. It does not describe the response format, but with no output schema and a straightforward list, this is a minor gap. Overall, the information provided is sufficient for an agent to call it correctly.
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 'region' parameters have clear descriptions in the schema. The tool description does not add any extra parameter semantics, but the baseline of 3 is appropriate since the schema already documents the parameters fully.
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 'Get TikTok search suggestions by query' – a specific verb and resource. It distinguishes from sibling search tools like tiktok_search_videos_list or tiktok_search_hashtag_list by focusing on 'suggestions' rather than hashtags, videos, or general search results.
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 usage: call when you need search suggestions for a query. However, it does not explicitly mention when not to use it or name alternatives among the many TikTok search tools. It relies on the word 'suggestions' to hint at specificity, but lacks direct guidance on when another search tool would be more appropriate.
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.