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datasets_creators_search

Search the TikTok creators dataset. Searches TikTok creators stored in a search index (one document per creator), with follower counts, verified status, niche, and engagement.

Instructions

Search the TikTok creators dataset. Searches TikTok creators stored in a search index (one document per creator), with follower counts, verified status, niche, and engagement. Deleted and private accounts are excluded by default; set include_inactive=true to include them for historical lookups. Sort enum: followers_desc, engagement_desc, engagement_qualified_desc, likes_desc, relevance. Coverage note: followers_desc, likes_desc, and relevance are backed by profile fields present across the full dataset; the post-level engagement metrics (engagement_rate, avg_views, and the nested post_stats object) and the engagement_desc/engagement_qualified_desc sorts are currently populated for a growing subset of creators, prioritizing the highest-reach accounts. Creators without these metrics are still returned but sort last under engagement_desc and omit those fields; engagement_qualified_desc excludes them outright (they cannot clear its floors). engagement_desc ranks by raw engagement_rate with no eligibility floor — it surfaces a real stale-record + ratio-by-design trap: an account whose last real post was years ago can still carry an unrealistic rate computed from a handful of old posts. engagement_qualified_desc is the same metric restricted to creators with a recent post (last_post_at within 90 days), a minimum reach (avg_views >= 10000) and sample size (post_stats.sampled_posts >= 10), and a sanity ceiling (engagement_rate <= 50%) — use this, not the raw sort, for a "best engagement" leaderboard. Sound fields: post_stats.top_sounds holds only a creator's FIVE most-used sounds from the sampled posts, ranked by use count with ties broken by lowest music_id, so it is a top-5 view and not the creator's full sound list; post_stats.distinct_sounds gives the true number of different sounds the sample used. Use each sound's original boolean to tell TikTok-generated original audio from catalogue tracks - do NOT infer it from the title, because TikTok localizes the original-audio label (sonido original, som original, оригинальный звук, and at least fifteen more), so a title match silently reclassifies original audio as named tracks. Billing: this search costs 1 credit per record returned, with a minimum of 2 (an empty result costs 2, a full page of 100 costs 100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over handle, nickname and bio, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: followers_desc, engagement_desc, engagement_qualified_desc, likes_desc, relevance. engagement_desc ranks by raw post-level engagement rate, currently populated for a subset of creators (highest-reach first); creators without it sort last. engagement_qualified_desc is the same metric restricted to creators with a recent post (<=90d), a minimum reach (avg_views>=10000) and sample size (>=10 posts), and a sanity ceiling (<=50%) -- use this, not the raw sort, for a 'best engagement' leaderboard
nicheNoExact content-niche filter, max 128 characters
handleNoExact handle lookup (case-insensitive), e.g. khaby.lame; returns the single creator with that exact @handle
countryNoExact creator country/region filter, max 128 characters
verifiedNoFilter by verified badge; true keeps only verified creators
has_emailNoFilter by contact-email presence; true keeps only creators with an email
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
include_emailNoReturn the stored contact email instead of a blanked value. Off by default for everyone, and honoured only for entitled (non-Free) API keys
min_followersNoMinimum follower count
include_inactiveNoInclude deleted/private accounts; defaults to false (only live accounts returned)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • changedInput schema / properties / sort / description
      Previous value: -"Sort enum: followers_desc, engagement_desc, likes_desc, relevance. engagement_desc ranks by post-level engagement rate, currently populated for a subset of creators (highest-reach first); creators without it sort last"New value: +"Sort enum: followers_desc, engagement_desc, engagement_qualified_desc, likes_desc, relevance. engagement_desc ranks by raw post-level engagement rate, currently populated for a subset of creators (highest-reach first); creators without it sort last. engagement_qualified_desc is the same metric restricted to creators with a recent post (<=90d), a minimum reach (avg_views>=10000) and sample size (>=10 posts), and a sanity ceiling (<=50%) -- use this, not the raw sort, for a 'best engagement' leaderboard"
    • addedInput schema / properties / sort / enum
      Added value: +[
      +  "followers_desc",
      +  "engagement_desc",
      +  "engagement_qualified_desc",
      +  "likes_desc",
      +  "relevance"
      +]
  2. Changed1 schema field changedv1.16.2
    • addedInput schema / properties / include_email
      Added value: +{
      +  "description": "Return the stored contact email instead of a blanked value. Off by default for everyone, and honoured only for entitled (non-Free) API keys",
      +  "type": "boolean"
      +}
  3. Addedv1.2.0

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and delivers: default exclusion of deleted/private accounts, coverage caveats for engagement metrics (subset, sort-last/excluded behavior), the stale-record + ratio-by-design trap, the exact qualification floors, and billing (1 credit per record, minimum 2, empty result = 2). This is unusually complete behavioral disclosure.

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-loads purpose and coverage before the deeper caveats, and nearly every sentence adds non-redundant information. It is long and the sort-layer explanation partially overlaps the schema's own sort description, which costs a point, but it is not padding.

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?

For a 12-parameter, annotation-free search with no output schema, the description covers filtering defaults, sort semantics, data coverage, and billing. It describes key returned fields (post_stats, original audio) but never sketches the return shape or pagination limits, leaving a small gap for a tool with no output schema.

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 100%, so the baseline is 3, but the description adds real semantics beyond the schema: why include_inactive matters (historical lookups), the meaning of the qualified vs raw engagement sorts, and the caveats on post_stats.top_sounds/distinct_sounds. These go past restating parameter names.

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

Purpose5/5

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

Opens with a specific verb+resource ('Search the TikTok creators dataset') and immediately scopes the store ('creators stored in a search index, one document per creator') with the fields it exposes (followers, verified status, niche, engagement). This clearly distinguishes it from siblings like datasets_instagram_users_search, datasets_youtube_creators_search, and tiktok_search.

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?

Gives strong conditional guidance: use engagement_qualified_desc 'not the raw sort, for a best engagement leaderboard', and set include_inactive=true only 'for historical lookups'. It does not, however, explicitly contrast this tool against the adjacent creators datasets (Instagram/YouTube) the way the calibration's high example routes to a named sibling.

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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