API reference

Demand and keywords

What people search for on TikTok, from TikTok's own Creator Search Insights, plus autocomplete and keyword ideas from TikTok and TikTok Shop.

16 routes
1 credit per call
Base URL https://api.tokconnect.com/v1

Find TikTok search topics

Finds what people search on TikTok (Creator Search Insights). Each topic has queryId, searchVolume (TikTok's 7-day popularity figure, never a monthly count), videoCount (0 = content gap) and a daily trend. Use for demand, content gaps and trend research. Pass a topic's queryId to demand_topic for charts, audience and related searches. Example: {"keyword":"protein snacks"} or {"method":"feed","channel":"content_gap"}.

GET/v1/searches

Topics matching keyword

1 credit per call
Paged

Parameters

keywordstring
method=search: phrase to match, e.g. 'bicep workouts'.
limitinteger
Results per page. Max 20. Values above a method's maximum are lowered and reported in notes.

3 more optional parameters

cursorstring
Paging: pass the next value from the previous response unchanged. Omit on the first page.
detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
languagestring
method=search or feed: language code from reference method=csi_languages, e.g. en.

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_find_topics tool with method: search. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_find_topics",
  "arguments": {
    "method": "search",
    "keyword": "lip oil",
    "limit": 3
  }
}

Request

curl "https://api.tokconnect.com/v1/searches?keyword=lip%20oil&limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches",
    params={
        "keyword": "lip oil",
        "limit": 3,
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  keyword: "lip oil",
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/searches?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "channel": "search",
    "hasMore": true,
    "next": "eyJvZmZzZXQiOjN9",
    "tab": "SEARCH",
    "topics": [
      {
        "category": "Fashion > Fashion Tutorials",
        "popularity": 77,
        "queryId": "7661799877281595413",
        "queryText": "what to look for in a lip oil",
        "searchVolume": 384507,
        "trend7d": [
          286756,
          201899,
          179210
        ],
        "videoCount": 0
      },
      {
        "category": "Fashion > Fashion Products > Beauty and Skin Care Products > Beauty Products",
        "popularity": 72,
        "queryId": "41561603",
        "queryText": "Best lip oil",
        "searchVolume": 189105,
        "trend7d": [
          185907,
          210094,
          137831
        ],
        "videoCount": 1113
      },
      {
        "category": "Fashion > Fashion Products > Beauty and Skin Care Products > Beauty Products",
        "popularity": 40,
        "queryId": "7411340654124498950",
        "queryText": "lip oils for lips",
        "searchVolume": 3799,
        "trend7d": [
          4099,
          3499,
          2999
        ],
        "videoCount": 1168
      }
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  },
  "next_cursor": "eyJvZmZzZXQiOjN9"
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.channelstring
data.hasMoreboolean
data.nextstring
data.tabstring
data.topicsarray
data.topics[].categorystring
data.topics[].popularityinteger
data.topics[].queryIdstring
data.topics[].queryTextstring
data.topics[].searchVolumeinteger
data.topics[].trend7darray
data.topics[].videoCountinteger
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/searches/feed

A ready-made list (channel)

1 credit per call
Paged

Parameters

limitinteger
Results per page. Max 20. Values above a method's maximum are lowered and reported in notes.

4 more optional parameters

channelstring
method=feed: which list. suggested, content_gap (high demand, few videos), or a category.
One ofsuggestedcontent_gaplifestylefashionfoodsportstourismgamingscience
cursorstring
Paging: pass the next value from the previous response unchanged. Omit on the first page.
detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
languagestring
method=search or feed: language code from reference method=csi_languages, e.g. en.

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_find_topics tool with method: feed. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_find_topics",
  "arguments": {
    "method": "feed",
    "limit": "3"
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/feed?limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/feed",
    params={
        "limit": "3",
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/searches/feed?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "hasMore": true,
    "next": "eyJvZmZzZXQiOjN9",
    "tab": "ALL",
    "topics": [
      {
        "category": "TikTok's Featured Content > Others TikTok and Video Contents",
        "popularity": 2,
        "queryId": "7637721127158022164",
        "queryText": "views and likes",
        "searchVolume": 99,
        "trend7d": [
          99,
          299,
          99
        ],
        "videoCount": 194769
      },
      {
        "category": "Local Life > Leisure and Entertainment > Other Leisure and Entertainment Venues",
        "popularity": 93,
        "queryId": "7693004027847507989",
        "queryText": "lucki complex con",
        "searchVolume": 34704706,
        "trend7d": [
          0,
          0,
          0
        ],
        "videoCount": 228448
      },
      {
        "category": "TikTok's Featured Content > Stories/Posting Captions",
        "popularity": 72,
        "queryId": "7478520405142011910",
        "queryText": "#relate",
        "searchVolume": 180718,
        "trend7d": [
          3000,
          700,
          54105
        ],
        "videoCount": 166276
      }
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  },
  "next_cursor": "eyJvZmZzZXQiOjN9"
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.hasMoreboolean
data.nextstring
data.tabstring
data.topicsarray
data.topics[].categorystring
data.topics[].popularityinteger
data.topics[].queryIdstring
data.topics[].queryTextstring
data.topics[].searchVolumeinteger
data.topics[].trend7darray
data.topics[].videoCountinteger
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

API keyKept in this tab only and sent only to api.tokconnect.com.

limit

More parameters (4)

channelcursordetaillanguage

GET/v1/searches/trending

1 credit per call
Paged

Parameters

limitinteger
Results per page. Max 20. Values above a method's maximum are lowered and reported in notes.

3 more optional parameters

categorystring
method=trending: category tab (default all).
One ofallFashionFoodSportsTourismGamingScience
cursorstring
Paging: pass the next value from the previous response unchanged. Omit on the first page.
detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_find_topics tool with method: trending. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_find_topics",
  "arguments": {
    "method": "trending",
    "limit": 3
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/trending?limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/trending",
    params={
        "limit": 3,
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/searches/trending?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "channel": "trending",
    "hasMore": true,
    "next": "eyJvZmZzZXQiOjN9",
    "tab": "all",
    "topics": [
      {
        "category": "Sports > Physical Sports > Sports News > Other Sports News",
        "popularity": 93,
        "queryId": "7701568755255476244",
        "queryText": "gabriel penalty miss ballon dor",
        "searchVolume": 33137737,
        "trend7d": [
          0,
          0,
          0
        ],
        "videoCount": 0
      },
      {
        "category": "Entertainment > Celebrity Entertainment > Celebrity Gossips > Celebrity Gossip News",
        "popularity": 92,
        "queryId": "7690715705234161684",
        "queryText": "taylor swift weddings photos",
        "searchVolume": 25630078,
        "trend7d": [
          1692253,
          593303,
          222388
        ],
        "videoCount": 100
      },
      {
        "category": "Sports > Physical Sports > Sports Competitions > Ball Games",
        "popularity": 92,
        "queryId": "7687094461397991444",
        "queryText": "arsenal vs fleetwood",
        "searchVolume": 23658364,
        "trend7d": [
          300,
          300,
          100
        ],
        "videoCount": 0
      }
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  },
  "next_cursor": "eyJvZmZzZXQiOjN9"
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.channelstring
data.hasMoreboolean
data.nextstring
data.tabstring
data.topicsarray
data.topics[].categorystring
data.topics[].popularityinteger
data.topics[].queryIdstring
data.topics[].queryTextstring
data.topics[].searchVolumeinteger
data.topics[].trend7darray
data.topics[].videoCountinteger
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

One TikTok search topic

Everything about one search topic (id = queryId from demand_find_topics): detail, daily popularity chart, searcher demographics, related searches, TikTok's content guidance and matching videos. Popularity values are TikTok's readings: never add days together or call them monthly searches. Example: {"id":"7261...","method":"popularity","days":"180","countries":["US"]}.

GET/v1/searches/{id}

searchVolume, trend, category, top countries

1 credit per call

Parameters

idstringPathRequired
queryId from demand_find_topics. Not a video id or keyword.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_topic tool with method: detail. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_topic",
  "arguments": {
    "method": "detail",
    "id": "7637721127158022164"
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/7637721127158022164" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/7637721127158022164",
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const res = await fetch("https://api.tokconnect.com/v1/searches/7637721127158022164", {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "topCountries": [
      {
        "country_code": "PH",
        "country_name": "Philippines"
      },
      {
        "country_code": "ID",
        "country_name": "Indonesia"
      },
      {
        "country_code": "NG",
        "country_name": "Nigeria"
      }
    ],
    "topic": {
      "category": "TikTok's Featured Content > Others TikTok and Video Contents",
      "popularity": 2,
      "queryId": "7637721127158022164",
      "queryText": "views and likes",
      "searchVolume": 99,
      "trend7d": [
        99,
        299,
        99
      ],
      "videoCount": 194769
    },
    "usesInsightsVideoEndpoint": false
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.topCountriesarray
data.topCountries[].country_codestring
data.topCountries[].country_namestring
data.topicobject
data.topic.categorystring
data.topic.popularityinteger
data.topic.queryIdstring
data.topic.queryTextstring
data.topic.searchVolumeinteger
data.topic.trend7darray
data.topic.videoCountinteger
data.usesInsightsVideoEndpointboolean
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/searches/{id}/audience

Age, gender and location of searchers

1 credit per call

Parameters

idstringPathRequired
queryId from demand_find_topics. Not a video id or keyword.

2 more optional parameters

daysstring
method=popularity or audience: window in days (default 30).
One of73060180
detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_topic tool with method: audience. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_topic",
  "arguments": {
    "method": "audience",
    "id": "7637721127158022164"
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/7637721127158022164/audience" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/7637721127158022164/audience",
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const res = await fetch("https://api.tokconnect.com/v1/searches/7637721127158022164/audience", {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "age": [
      {
        "category": "total",
        "popularity": 96667
      },
      {
        "category": "25-34",
        "popularity": 32190
      },
      {
        "category": "18-24",
        "popularity": 26891
      }
    ],
    "gender": [
      {
        "category": "total",
        "popularity": 96669
      },
      {
        "category": "unknown",
        "popularity": 50284
      },
      {
        "category": "male",
        "popularity": 24492
      }
    ],
    "location": [
      {
        "category": "total",
        "popularity": 96586
      },
      {
        "category": "Indonesia",
        "popularity": 12896
      },
      {
        "category": "Pakistan",
        "popularity": 10696
      }
    ],
    "summary": {
      "age": "25-34",
      "gender": "unknown",
      "location": "Indonesia"
    }
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.agearray
data.age[].categorystring
data.age[].popularityinteger
data.genderarray
data.gender[].categorystring
data.gender[].popularityinteger
data.locationarray
data.location[].categorystring
data.location[].popularityinteger
data.summaryobject
data.summary.agestring
data.summary.genderstring
data.summary.locationstring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/searches/{id}/guidance

TikTok's suggested hooks, hashtags and outlines (often unavailable)

1 credit per call

Parameters

idstringPathRequired
queryId from demand_find_topics. Not a video id or keyword.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_topic tool with method: guidance. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_topic",
  "arguments": {
    "method": "guidance",
    "id": "7637721127158022164"
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/7637721127158022164/guidance" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/7637721127158022164/guidance",
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const res = await fetch("https://api.tokconnect.com/v1/searches/7637721127158022164/guidance", {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "available": false,
    "message": "TikTok reports that content guidance is not generated for this CSI topic yet. No guidance is available in this response."
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.availableboolean
data.messagestring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/searches/{id}/popularity

Daily popularity chart, worldwide or per country

1 credit per call

Parameters

idstringPathRequired
queryId from demand_find_topics. Not a video id or keyword.

3 more optional parameters

countriesarray of string
method=popularity: country codes to break out, e.g. "US","GB". Omit for worldwide.
daysstring
method=popularity or audience: window in days (default 30).
One of73060180
detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_topic tool with method: popularity. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_topic",
  "arguments": {
    "method": "popularity",
    "id": "7637721127158022164"
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/7637721127158022164/popularity" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/7637721127158022164/popularity",
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const res = await fetch("https://api.tokconnect.com/v1/searches/7637721127158022164/popularity", {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "series": [
      {
        "country": "global",
        "points": [
          {
            "date": "2026-09-30",
            "popularity": 1481
          },
          {
            "date": "2026-10-01",
            "popularity": 1093
          },
          {
            "date": "2026-10-02",
            "popularity": 701
          }
        ]
      }
    ],
    "windowDays": 30
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.seriesarray
data.series[].countrystring
data.series[].pointsarray
data.series[].points[].datestring
data.series[].points[].popularityinteger
data.windowDaysinteger
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/searches/{id}/related

1 credit per call
Paged

Parameters

idstringPathRequired
queryId from demand_find_topics. Not a video id or keyword.
limitinteger
Results per page. method=related max 20; method=videos max 20. Values above a method's maximum are lowered and reported in notes.

2 more optional parameters

cursorstring
Paging: pass the next value from the previous response unchanged. Omit on the first page.
detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_topic tool with method: related. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_topic",
  "arguments": {
    "method": "related",
    "id": "7637721127158022164",
    "limit": 3
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/7637721127158022164/related?limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/7637721127158022164/related",
    params={
        "limit": 3,
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/searches/7637721127158022164/related?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "hasMore": true,
    "next": "eyJvZmZzZXQiOjN9",
    "topics": [
      {
        "category": "Fashion > Fashion Tutorials > Beauty and Skin Care Tutorials > Beauty Tutorials",
        "popularity": 4,
        "queryId": "7482230116668489734",
        "queryText": "Make. Up",
        "searchVolume": 199,
        "trend7d": [
          0,
          199,
          99
        ],
        "videoCount": 8549
      },
      {
        "category": "Science and Technology > Internet > Internet Entrepreneurship Knowledge > Self-Media Operation Knowledge",
        "popularity": 72,
        "queryId": "7417365957699534853",
        "queryText": "More likes",
        "searchVolume": 181454,
        "trend7d": [
          124737,
          176552,
          44413
        ],
        "videoCount": 408803
      },
      {
        "category": "Science and Technology > Internet > Internet Entrepreneurship Knowledge > Self-Media Operation Knowledge",
        "popularity": 65,
        "queryId": "7411340955263123461",
        "queryText": "more views",
        "searchVolume": 50974,
        "trend7d": [
          121239,
          2898,
          12693
        ],
        "videoCount": 29439
      }
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  },
  "next_cursor": "eyJvZmZzZXQiOjN9"
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.hasMoreboolean
data.nextstring
data.topicsarray
data.topics[].categorystring
data.topics[].popularityinteger
data.topics[].queryIdstring
data.topics[].queryTextstring
data.topics[].searchVolumeinteger
data.topics[].trend7darray
data.topics[].videoCountinteger
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/searches/{id}/related-queries

1 credit per call

Parameters

idstringPathRequired
queryId from demand_find_topics. Not a video id or keyword.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_topic tool with method: related_queries. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_topic",
  "arguments": {
    "method": "related_queries",
    "id": "7637721127158022164"
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/7637721127158022164/related-queries" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/7637721127158022164/related-queries",
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const res = await fetch("https://api.tokconnect.com/v1/searches/7637721127158022164/related-queries", {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "queries": []
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.queriesarray
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/searches/{id}/videos

Videos TikTok shows for the topic

1 credit per call
Paged

Parameters

idstringPathRequired
queryId from demand_find_topics. Not a video id or keyword.
limitinteger
Results per page. method=related max 20; method=videos max 20. Values above a method's maximum are lowered and reported in notes.

2 more optional parameters

cursorstring
Paging: pass the next value from the previous response unchanged. Omit on the first page.
detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_topic tool with method: videos. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_topic",
  "arguments": {
    "method": "videos",
    "id": "7637721127158022164",
    "limit": "3"
  }
}

Request

curl "https://api.tokconnect.com/v1/searches/7637721127158022164/videos?limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/searches/7637721127158022164/videos",
    params={
        "limit": "3",
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/searches/7637721127158022164/videos?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "hasMore": true,
    "next": "eyJvZmZzZXQiOjN9",
    "topicQueryId": "7637721127158022164",
    "topicQueryText": "views and likes",
    "videos": [
      {
        "caption": "going viral does NOT have to be super complicated!! #iwanttogoviral #howtogoviralontiktok #howtogoviraltips #tiktokgrowth",
        "comments": 55778,
        "creator": {
          "followers": 18459,
          "handle": "thelifeofjftb",
          "id": "6895909988976395270",
          "nickname": "jenny",
          "secUid": "MS4wLjABAAAAdhjwJHeb-b3JtDSrR-E8hiKgVLnfHukcHXlpylkukO23nDO8Pgib43DTfy9kayiV"
        },
        "durationSec": 89.634,
        "hashtags": [
          {
            "id": "85581100",
            "name": "iwanttogoviral"
          },
          {
            "id": "1645041228246018",
            "name": "howtogoviralontiktok"
          },
          {
            "id": "1661640432713733",
            "name": "howtogoviraltips"
          }
        ],
        "id": "7516402158825196846",
        "language": "en",
        "likes": 200636,
        "posted": "2025-06-16T04:39:53Z",
        "saves": 78023,
        "shares": 75002,
        "sound": {
          "author": "jenny",
          "id": "7516402224461892395",
          "original": true,
          "title": "original sound - thelifeofjftb"
        },
        "url": "https://www.tiktok.com/@thelifeofjftb/video/7516402158825196846",
        "views": 2086466
      },
      {
        "caption": "The views may be low but unfortunately I’ve already decided I’m famous 😭😂 #TikTokHumor #SmallCreator #MainCharacterEnergy #ForYou #KeepPosting",
        "comments": 8422,
        "creator": {
          "followers": 2868,
          "handle": "thehouseofsamuel",
          "id": "7605020458355196941",
          "nickname": "The House Of Samuel",
          "secUid": "MS4wLjABAAAAoJkre_H8s5vnyPV8BRX1hTk1-vrY-VkKYFvBTKCAh3Q2vlpwN80nZL0hHlhtldNA"
        },
        "durationSec": 6.547,
        "hashtags": [
          {
            "id": "1589764631152642",
            "name": "tiktokhumor"
          },
          {
            "id": "1609436969656325",
            "name": "smallcreator"
          },
          {
            "id": "1665817412595717",
            "name": "maincharacterenergy"
          }
        ],
        "id": "7693916067256749343",
        "language": "en",
        "likes": 157523,
        "posted": "2026-10-07T13:24:33Z",
        "saves": 22839,
        "shares": 8585,
        "sound": {
          "author": "AngieBLoveee🇺🇸",
          "id": "7414631646038625066",
          "original": true,
          "title": "Imma keep posting"
        },
        "url": "https://www.tiktok.com/@thehouseofsamuel/video/7693916067256749343",
        "views": 1088039
      },
      {
        "caption": "Here’s how to use the new algorithm to get more likes and views on your posts! #creatorsearchinsights  #howtogoviral",
        "comments": 283,
        "creator": {
          "followers": 154056,
          "handle": "beckett_cj",
          "id": "7471419225988236334",
          "nickname": "Beckett CJ",
          "secUid": "MS4wLjABAAAAwnFwtUWXMkbHwZB7aKcCFKjdg6eGpv7h3mrjqad3HMrtjm2zXL0DZ4IHUB0cXWLp"
        },
        "durationSec": 78.668,
        "hashtags": [
          {
            "id": "7340824418278440993",
            "name": "creatorsearchinsights"
          },
          {
            "id": "1620762388052997",
            "name": "howtogoviral"
          }
        ],
        "id": "7623207520521194765",
        "language": "en",
        "likes": 2068,
        "posted": "2026-03-31T00:18:50Z",
        "saves": 979,
        "shares": 332,
        "sound": {
          "author": "Beckett CJ",
          "id": "7623207565551209229",
          "original": true,
          "title": "original sound - beckett_cj"
        },
  …

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.hasMoreboolean
data.nextstring
data.topicQueryIdstring
data.topicQueryTextstring
data.videosarray
data.videos[].captionstring
data.videos[].commentsinteger
data.videos[].creatorobject
data.videos[].creator.followersinteger
data.videos[].creator.handlestring
data.videos[].creator.idstring
data.videos[].creator.nicknamestring
data.videos[].creator.secUidstring
data.videos[].durationSecnumber
data.videos[].hashtagsarray
data.videos[].hashtags[].idstring
data.videos[].hashtags[].namestring
data.videos[].idstring
data.videos[].languagestring
data.videos[].likesinteger
data.videos[].postedstring
data.videos[].savesinteger
data.videos[].sharesinteger
data.videos[].soundobject
data.videos[].sound.authorstring
data.videos[].sound.idstring
data.videos[].sound.originalboolean
data.videos[].sound.titlestring
data.videos[].urlstring
data.videos[].viewsinteger
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

Keyword suggestions

Autocomplete and keyword ideas from TikTok and TikTok Shop. method=signals adds TikTok's predicted click-through and a shopping-intent flag per phrase. These rank phrases; they are not search volume (use demand_find_topics for volume). Example: {"keyword":"matcha","method":"signals"}.

GET/v1/keywords/accounts

Account (@handle) suggestions

1 credit per call

Parameters

keywordstring
Seed phrase. Not needed for method=trending.
limitinteger
Results per page. tiktok/accounts max 50, signals max 20, trending max 30, ad_library max 20. Values above a method's maximum are lowered and reported in notes.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_keywords tool with method: accounts. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_keywords",
  "arguments": {
    "method": "accounts",
    "keyword": "lip oil",
    "limit": 3
  }
}

Request

curl "https://api.tokconnect.com/v1/keywords/accounts?keyword=lip%20oil&limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/keywords/accounts",
    params={
        "keyword": "lip oil",
        "limit": 3,
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  keyword: "lip oil",
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/keywords/accounts?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "suggestions": [
      "lip oil",
      "Lip Oil",
      "Lip oil"
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.suggestionsarray
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/keywords/ad-library

EU/UK Ad Library advertiser and keyword autocomplete

1 credit per call
17 markets

Parameters

keywordstring
Seed phrase. Not needed for method=trending.
limitinteger
Results per page. tiktok/accounts max 50, signals max 20, trending max 30, ad_library max 20. Values above a method's maximum are lowered and reported in notes.

3 more optional parameters

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
regionstring
method=ad_library: 2-letter EU/UK country from reference method=ad_regions. Default DE.
suggest_typestring
method=ad_library: TikTok's suggestion type, default 1 (advertisers and keywords).

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_keywords tool with method: ad_library. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_keywords",
  "arguments": {
    "method": "ad_library",
    "keyword": "lip oil",
    "limit": 3
  }
}

Request

curl "https://api.tokconnect.com/v1/keywords/ad-library?keyword=lip%20oil&limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/keywords/ad-library",
    params={
        "keyword": "lip oil",
        "limit": 3,
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  keyword: "lip oil",
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/keywords/ad-library?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "advertisers": [],
    "code": 0,
    "keywords": []
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.advertisersarray
data.codeinteger
data.keywordsarray
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/keywords/shop

TikTok Shop search autocomplete

1 credit per call

Parameters

keywordstring
Seed phrase. Not needed for method=trending.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_keywords tool with method: shop. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_keywords",
  "arguments": {
    "method": "shop",
    "keyword": "lip oil"
  }
}

Request

curl "https://api.tokconnect.com/v1/keywords/shop?keyword=lip%20oil" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/keywords/shop",
    params={
        "keyword": "lip oil",
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  keyword: "lip oil",
});
const res = await fetch(`https://api.tokconnect.com/v1/keywords/shop?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "page": "suggest",
    "suggestions": [
      "lip oil hydration",
      "lip oil stain",
      "lip oil gloss"
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.pagestring
data.suggestionsarray
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/keywords/signals

Autocomplete with click-through and shopping-intent signals

1 credit per call

Parameters

keywordstring
Seed phrase. Not needed for method=trending.
limitinteger
Results per page. tiktok/accounts max 50, signals max 20, trending max 30, ad_library max 20. Values above a method's maximum are lowered and reported in notes.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_keywords tool with method: signals. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_keywords",
  "arguments": {
    "method": "signals",
    "keyword": "lip oil",
    "limit": 3
  }
}

Request

curl "https://api.tokconnect.com/v1/keywords/signals?keyword=lip%20oil&limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/keywords/signals",
    params={
        "keyword": "lip oil",
        "limit": 3,
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  keyword: "lip oil",
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/keywords/signals?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "keyword": "lip oil",
    "suggestions": [
      {
        "ecomIntent": 2,
        "predictedCtr": 0.026337544,
        "text": "lip oil"
      },
      {
        "ecomIntent": 1,
        "predictedCtr": 0.0067250463,
        "text": "lip oil vs lip gloss"
      },
      {
        "ecomIntent": 2,
        "predictedCtr": 0.0041258996,
        "text": "Lip oil essence"
      }
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.keywordstring
data.suggestionsarray
data.suggestions[].ecomIntentinteger
data.suggestions[].predictedCtrnumber
data.suggestions[].textstring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/keywords/suggest

TikTok search autocomplete

1 credit per call

Parameters

keywordstring
Seed phrase. Not needed for method=trending.
limitinteger
Results per page. tiktok/accounts max 50, signals max 20, trending max 30, ad_library max 20. Values above a method's maximum are lowered and reported in notes.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_keywords tool with method: tiktok. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_keywords",
  "arguments": {
    "method": "tiktok",
    "keyword": "lip oil",
    "limit": 3
  }
}

Request

curl "https://api.tokconnect.com/v1/keywords/suggest?keyword=lip%20oil&limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/keywords/suggest",
    params={
        "keyword": "lip oil",
        "limit": 3,
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  keyword: "lip oil",
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/keywords/suggest?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "suggestions": [
      "lip oil",
      "lip oil vs lip gloss",
      "Lip oil essence"
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.suggestionsarray
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/keywords/trending

1 credit per call

Parameters

limitinteger
Results per page. tiktok/accounts max 50, signals max 20, trending max 30, ad_library max 20. Values above a method's maximum are lowered and reported in notes.

1 more optional parameter

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the demand_keywords tool with method: trending. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "demand_keywords",
  "arguments": {
    "method": "trending",
    "limit": "3"
  }
}

Request

curl "https://api.tokconnect.com/v1/keywords/trending?limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/keywords/trending",
    params={
        "limit": "3",
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/keywords/trending?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "searches": [
      "Hyena makeup for Halloween trend",
      "Band Aid and 2 Pimple Patches remarks trend",
      "Fancy Footwork trend"
    ]
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.searchesarray
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit