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Search TT Videos

search_tt_videos
Read-onlyIdempotent

[read] Find TikTok videos and image posts for a keyword (source SEARCH: keyword search, HASHTAG: the posts of the hashtag the keyword names). Each video has aweme_id, title and user_id; users lists those authors (user_id, username, nickname); region (two letters) and language filter the result. Pass the aweme_ids you want studied to analyze_tt_trend_media. The first page is one priced search.

Input Schema

NameRequiredDescriptionDefault
countNo
offsetNo
regionNo
sourceNoHASHTAG
keywordYes
languageNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover readOnly/openWorld/idempotent/non-destructive, so the bar is lower; the description still adds the critical cost trait 'The first page is one priced search,' which no annotation conveys. It also discloses the shape of what comes back (aweme_id, title, user_id, users with username/nickname). It does not address rate limits or whether subsequent pages cost the same.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the verb and resource, then layered with mode semantics, return shape, and cost — every clause carries information. The parenthetical and semicolon-heavy packing make it denser than it needs to be, but nothing is filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, describing the returned fields and the paired `users` list is genuinely necessary and is done here. Cost and downstream handoff are covered. The gap is pagination/limit behavior for `count` and `offset` on a tool explicitly priced per page.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries the burden — it defines `source` semantics, `region` as a two-letter code, `language` as a result filter, and `keyword` implicitly through the SEARCH/HASHTAG explanation. `count` and `offset` are only hinted at via 'The first page', leaving pagination parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

A specific verb and resource: 'Find TikTok videos and image posts for a keyword', with the two `source` modes spelled out (SEARCH = keyword search, HASHTAG = posts of the named hashtag). It routes the follow-up work to analyze_tt_trend_media, but never distinguishes itself from the sibling list_tt_videos, which an agent could plausibly pick for the same sounding task.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear context is given: use this to collect video IDs, then 'Pass the `aweme_id`s you want studied to analyze_tt_trend_media.' The SEARCH vs HASHTAG explanation also tells the agent which mode to choose. There is no explicit when-not-to-use statement and no mention of when list_tt_videos would be preferable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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