Module airbyte_agent_sdk.connectors.tiktok_marketing.connector

Tiktok-Marketing connector.

Classes

AdGroupsQuery(connector: TiktokMarketingConnector) : Query class for AdGroups entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdGroupsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdGroupsSearchData] : Search ad_groups records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdGroupsSearchFilter):

adgroup_id: The unique identifier of the ad group
adgroup_name: The name of the ad group
advertiser_id: The unique identifier of the advertiser
budget: The allocated budget for the ad group
budget_mode: The mode for managing the budget
campaign_id: The unique identifier of the campaign
create_time: The timestamp for when the ad group was created
modify_time: The timestamp for when the ad group was last modified
operation_status: The status of the operation
optimization_goal: The goal set for optimization
placement_type: The type of ad placement
promotion_type: The type of promotion
secondary_status: The secondary status of the ad group

Args: query: Filter and sort conditions. Supports operators such as eq, neq, gt, gte, lt, lte, in, startswith, endswith, contains, array_contains, fuzzy, keyword, not, and, or. Example: {"filter": {"eq": {"status": "active"}}} limit: Maximum results to return (default 1000) cursor: Pagination cursor from previous response's meta.cursor fields: Field paths to include in results. Each path is a list of keys for nested access. Example: [["id"], ["user", "name"]] returns id and user.name fields.

Returns: AdGroupsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

context_store_sql_query(self, sql: str, limit: int | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[dict[str, Any]] : Run a SQL query against ad_groups records in the Airbyte Context Store.

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[AdGroup], AdGroupsListResultMeta] : Get ad groups for an advertiser

Args: advertiser_id: Advertiser ID page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: AdGroupsListResult

AdGroupsReportsDailyQuery(connector: TiktokMarketingConnector) : Query class for AdGroupsReportsDaily entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdGroupsReportsDailySearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdGroupsReportsDailySearchData] : Search ad_groups_reports_daily records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdGroupsReportsDailySearchFilter):

adgroup_id: The unique identifier for the ad group.
stat_time_day: The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).
campaign_name: The name of the marketing campaign.
campaign_id: The unique identifier for the campaign.
adgroup_name: The name of the ad group.
placement_type: Type of ad placement.
spend: Total amount of money spent.
cpc: Cost per click.
cpm: Cost per thousand impressions.
impressions: Number of times the ad was displayed.
clicks: Number of clicks on the ad.
ctr: Click-through rate.
reach: Total number of unique users reached.
cost_per_1000_reached: Cost per 1000 unique users reached.
conversion: Number of conversions.
cost_per_conversion: Cost per conversion.
conversion_rate: Rate of conversions.
real_time_conversion: Real-time conversions.
real_time_cost_per_conversion: Real-time cost per conversion.
real_time_conversion_rate: Real-time conversion rate.
result: Number of results.
cost_per_result: Cost per result.
result_rate: Rate of results.
real_time_result: Real-time results.
real_time_cost_per_result: Real-time cost per result.
real_time_result_rate: Real-time result rate.
secondary_goal_result: Results for secondary goals.
cost_per_secondary_goal_result: Cost per secondary goal result.
secondary_goal_result_rate: Rate of secondary goal results.
frequency: Average number of times each person saw the ad.
video_play_actions: Number of video play actions.
video_watched_2s: Number of times video was watched for at least 2 seconds.
video_watched_6s: Number of times video was watched for at least 6 seconds.
average_video_play: Average video play duration.
average_video_play_per_user: Average video play duration per user.
video_views_p25: Number of times video was watched to 25%.
video_views_p50: Number of times video was watched to 50%.
video_views_p75: Number of times video was watched to 75%.
video_views_p100: Number of times video was watched to 100%.
profile_visits: Number of profile visits.
likes: Number of likes.
comments: Number of comments.
shares: Number of shares.
follows: Number of follows.
clicks_on_music_disc: Number of clicks on the music disc.
real_time_app_install: Real-time app installations.
real_time_app_install_cost: Cost of real-time app installations.
app_install: Number of app installations.

Returns: AdGroupsReportsDailySearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, service_type: str, report_type: str, data_level: str, dimensions: str, metrics: str, start_date: str, end_date: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[AdGroupsReportDaily], AdGroupsReportsDailyListResultMeta] : Get daily performance reports at the ad group level

Args: advertiser_id: Advertiser ID service_type: Service type report_type: Report type data_level: Data level for the report dimensions: Dimensions for the report (JSON array) metrics: Metrics to retrieve (JSON array) start_date: Report start date (YYYY-MM-DD) end_date: Report end date (YYYY-MM-DD) page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: AdGroupsReportsDailyListResult

AdsQuery(connector: TiktokMarketingConnector) : Query class for Ads entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdsSearchData] : Search ads records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdsSearchFilter):

ad_format: The format of the ad
ad_id: The unique identifier of the ad
ad_name: The name of the ad
ad_text: The text content of the ad
adgroup_id: The unique identifier of the ad group
adgroup_name: The name of the ad group
advertiser_id: The unique identifier of the advertiser
campaign_id: The unique identifier of the campaign
campaign_name: The name of the campaign
create_time: The timestamp when the ad was created
landing_page_url: The URL of the landing page for the ad
modify_time: The timestamp when the ad was last modified
operation_status: The operational status of the ad
secondary_status: The secondary status of the ad
video_id: The unique identifier of the video

Returns: AdsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

Args: advertiser_id: Advertiser ID page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: AdsListResult

AdsReportsDailyQuery(connector: TiktokMarketingConnector) : Query class for AdsReportsDaily entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdsReportsDailySearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdsReportsDailySearchData] : Search ads_reports_daily records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdsReportsDailySearchFilter):

ad_id: The unique identifier for the ad.
stat_time_day: The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).
campaign_name: The name of the marketing campaign.
campaign_id: The unique identifier for the campaign.
adgroup_name: The name of the ad group.
adgroup_id: The unique identifier for the ad group.
ad_name: The name of the ad.
ad_text: The text content of the ad.
placement_type: Type of ad placement.
spend: Total amount of money spent.
cpc: Cost per click.
cpm: Cost per thousand impressions.
impressions: Number of times the ad was displayed.
clicks: Number of clicks on the ad.
ctr: Click-through rate.
reach: Total number of unique users reached.
cost_per_1000_reached: Cost per 1000 unique users reached.
conversion: Number of conversions.
cost_per_conversion: Cost per conversion.
conversion_rate: Rate of conversions.
real_time_conversion: Real-time conversions.
real_time_cost_per_conversion: Real-time cost per conversion.
real_time_conversion_rate: Real-time conversion rate.
result: Number of results.
cost_per_result: Cost per result.
result_rate: Rate of results.
real_time_result: Real-time results.
real_time_cost_per_result: Real-time cost per result.
real_time_result_rate: Real-time result rate.
secondary_goal_result: Results for secondary goals.
cost_per_secondary_goal_result: Cost per secondary goal result.
secondary_goal_result_rate: Rate of secondary goal results.
frequency: Average number of times each person saw the ad.
video_play_actions: Number of video play actions.
video_watched_2s: Number of times video was watched for at least 2 seconds.
video_watched_6s: Number of times video was watched for at least 6 seconds.
average_video_play: Average video play duration.
average_video_play_per_user: Average video play duration per user.
video_views_p25: Number of times video was watched to 25%.
video_views_p50: Number of times video was watched to 50%.
video_views_p75: Number of times video was watched to 75%.
video_views_p100: Number of times video was watched to 100%.
profile_visits: Number of profile visits.
likes: Number of likes.
comments: Number of comments.
shares: Number of shares.
follows: Number of follows.
clicks_on_music_disc: Number of clicks on the music disc.
real_time_app_install: Real-time app installations.
real_time_app_install_cost: Cost of real-time app installations.
app_install: Number of app installations.

Returns: AdsReportsDailySearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, service_type: str, report_type: str, data_level: str, dimensions: str, metrics: str, start_date: str, end_date: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[AdsReportDaily], AdsReportsDailyListResultMeta] : Get daily performance reports at the ad level

Returns: AdsReportsDailyListResult

AdsReportsHourlyQuery(connector: TiktokMarketingConnector) : Query class for AdsReportsHourly entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdsReportsHourlySearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdsReportsHourlySearchData] : Search ads_reports_hourly records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdsReportsHourlySearchFilter):

ad_id: The unique identifier for the ad.
stat_time_hour: The hour for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).
campaign_name: The name of the marketing campaign.
campaign_id: The unique identifier for the campaign.
adgroup_name: The name of the ad group.
adgroup_id: The unique identifier for the ad group.
ad_name: The name of the ad.
ad_text: The text content of the ad.
placement_type: Type of ad placement.
spend: Total amount of money spent.
cpc: Cost per click.
cpm: Cost per thousand impressions.
impressions: Number of times the ad was displayed.
clicks: Number of clicks on the ad.
ctr: Click-through rate.
reach: Total number of unique users reached.
cost_per_1000_reached: Cost per 1000 unique users reached.
conversion: Number of conversions.
cost_per_conversion: Cost per conversion.
conversion_rate: Rate of conversions.
real_time_conversion: Real-time conversions.
real_time_cost_per_conversion: Real-time cost per conversion.
real_time_conversion_rate: Real-time conversion rate.
result: Number of results.
cost_per_result: Cost per result.
result_rate: Rate of results.
real_time_result: Real-time results.
real_time_cost_per_result: Real-time cost per result.
real_time_result_rate: Real-time result rate.
secondary_goal_result: Results for secondary goals.
cost_per_secondary_goal_result: Cost per secondary goal result.
secondary_goal_result_rate: Rate of secondary goal results.
frequency: Average number of times each person saw the ad.
video_play_actions: Number of video play actions.
video_watched_2s: Number of times video was watched for at least 2 seconds.
video_watched_6s: Number of times video was watched for at least 6 seconds.
average_video_play: Average video play duration.
average_video_play_per_user: Average video play duration per user.
video_views_p25: Number of times video was watched to 25%.
video_views_p50: Number of times video was watched to 50%.
video_views_p75: Number of times video was watched to 75%.
video_views_p100: Number of times video was watched to 100%.
profile_visits: Number of profile visits.
likes: Number of likes.
comments: Number of comments.
shares: Number of shares.
follows: Number of follows.
clicks_on_music_disc: Number of clicks on the music disc.
real_time_app_install: Real-time app installations.
real_time_app_install_cost: Cost of real-time app installations.
app_install: Number of app installations.

Returns: AdsReportsHourlySearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, service_type: str, report_type: str, data_level: str, dimensions: str, metrics: str, start_date: str, end_date: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[AdsReportHourly], AdsReportsHourlyListResultMeta] : Get hourly performance reports at the ad level

Returns: AdsReportsHourlyListResult

AdsReportsLifetimeQuery(connector: TiktokMarketingConnector) : Query class for AdsReportsLifetime entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdsReportsLifetimeSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdsReportsLifetimeSearchData] : Search ads_reports_lifetime records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdsReportsLifetimeSearchFilter):

ad_id: The unique identifier for the ad.
campaign_name: The name of the marketing campaign.
campaign_id: The unique identifier for the campaign.
adgroup_name: The name of the ad group.
adgroup_id: The unique identifier for the ad group.
ad_name: The name of the ad.
ad_text: The text content of the ad.
placement_type: Type of ad placement.
spend: Total amount of money spent.
cpc: Cost per click.
cpm: Cost per thousand impressions.
impressions: Number of times the ad was displayed.
clicks: Number of clicks on the ad.
ctr: Click-through rate.
reach: Total number of unique users reached.
cost_per_1000_reached: Cost per 1000 unique users reached.
conversion: Number of conversions.
cost_per_conversion: Cost per conversion.
conversion_rate: Rate of conversions.
real_time_conversion: Real-time conversions.
real_time_cost_per_conversion: Real-time cost per conversion.
real_time_conversion_rate: Real-time conversion rate.
result: Number of results.
cost_per_result: Cost per result.
result_rate: Rate of results.
real_time_result: Real-time results.
real_time_cost_per_result: Real-time cost per result.
real_time_result_rate: Real-time result rate.
secondary_goal_result: Results for secondary goals.
cost_per_secondary_goal_result: Cost per secondary goal result.
secondary_goal_result_rate: Rate of secondary goal results.
frequency: Average number of times each person saw the ad.
video_play_actions: Number of video play actions.
video_watched_2s: Number of times video was watched for at least 2 seconds.
video_watched_6s: Number of times video was watched for at least 6 seconds.
average_video_play: Average video play duration.
average_video_play_per_user: Average video play duration per user.
video_views_p25: Number of times video was watched to 25%.
video_views_p50: Number of times video was watched to 50%.
video_views_p75: Number of times video was watched to 75%.
video_views_p100: Number of times video was watched to 100%.
profile_visits: Number of profile visits.
likes: Number of likes.
comments: Number of comments.
shares: Number of shares.
follows: Number of follows.
clicks_on_music_disc: Number of clicks on the music disc.
real_time_app_install: Real-time app installations.
real_time_app_install_cost: Cost of real-time app installations.
app_install: Number of app installations.

Returns: AdsReportsLifetimeSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, service_type: str, report_type: str, data_level: str, dimensions: str, metrics: str, start_date: str, end_date: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[AdsReportLifetime], AdsReportsLifetimeListResultMeta] : Get lifetime performance reports at the ad level

Returns: AdsReportsLifetimeListResult

AdvertisersQuery(connector: TiktokMarketingConnector) : Query class for Advertisers entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdvertisersSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdvertisersSearchData] : Search advertisers records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdvertisersSearchFilter):

address: The physical address of the advertiser.
advertiser_account_type: The type of advertiser's account (e.g., individual, business).
advertiser_id: Unique identifier for the advertiser.
balance: The current balance in the advertiser's account.
brand: The brand name associated with the advertiser.
cellphone_number: The cellphone number of the advertiser.
company: The name of the company associated with the advertiser.
contacter: The contact person for the advertiser.
country: The country where the advertiser is located.
create_time: The timestamp when the advertiser account was created.
currency: The currency used for transactions in the account.
description: A brief description or bio of the advertiser or company.
display_timezone: The timezone for display purposes.
email: The email address associated with the advertiser.
industry: The industry or sector the advertiser operates in.
language: The preferred language of communication for the advertiser.
license_city: The city where the advertiser's license is registered.
license_no: The license number of the advertiser.
license_province: The province or state where the advertiser's license is registered.
license_url: The URL link to the advertiser's license documentation.
name: The name of the advertiser or company.
promotion_area: The specific area or region where the advertiser focuses promotion.
promotion_center_city: The city at the center of the advertiser's promotion activities.
promotion_center_province: The province or state at the center of the advertiser's promotion activities.
rejection_reason: Reason for any advertisement rejection by the platform.
role: The role or position of the advertiser within the company.
status: The current status of the advertiser's account.
telephone_number: The telephone number of the advertiser.
timezone: The timezone setting for the advertiser's activities.

Returns: AdvertisersSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_ids: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[Advertiser], AdvertisersListResultMeta] : Get advertiser account information

Args: advertiser_ids: Advertiser IDs (JSON array of strings) page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: AdvertisersListResult

AdvertisersReportsDailyQuery(connector: TiktokMarketingConnector) : Query class for AdvertisersReportsDaily entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AdvertisersReportsDailySearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdvertisersReportsDailySearchData] : Search advertisers_reports_daily records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AdvertisersReportsDailySearchFilter):

advertiser_id: The unique identifier for the advertiser.
stat_time_day: The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).
spend: Total amount of money spent.
cash_spend: The amount of money spent in cash.
voucher_spend: Amount spent using vouchers.
cpc: Cost per click.
cpm: Cost per thousand impressions.
impressions: Number of times the ad was displayed.
clicks: Number of clicks on the ad.
ctr: Click-through rate.
reach: Total number of unique users reached.
cost_per_1000_reached: Cost per 1000 unique users reached.
frequency: Average number of times each person saw the ad.
video_play_actions: Number of video play actions.
video_watched_2s: Number of times video was watched for at least 2 seconds.
video_watched_6s: Number of times video was watched for at least 6 seconds.
average_video_play: Average video play duration.
average_video_play_per_user: Average video play duration per user.
video_views_p25: Number of times video was watched to 25%.
video_views_p50: Number of times video was watched to 50%.
video_views_p75: Number of times video was watched to 75%.
video_views_p100: Number of times video was watched to 100%.
profile_visits: Number of profile visits.
likes: Number of likes.
comments: Number of comments.
shares: Number of shares.
follows: Number of follows.
clicks_on_music_disc: Number of clicks on the music disc.
real_time_app_install: Real-time app installations.
real_time_app_install_cost: Cost of real-time app installations.
app_install: Number of app installations.

Returns: AdvertisersReportsDailySearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, service_type: str, report_type: str, data_level: str, dimensions: str, metrics: str, start_date: str, end_date: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[AdvertisersReportDaily], AdvertisersReportsDailyListResultMeta] : Get daily performance reports at the advertiser level

Returns: AdvertisersReportsDailyListResult

AudiencesQuery(connector: TiktokMarketingConnector) : Query class for Audiences entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: AudiencesSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AudiencesSearchData] : Search audiences records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AudiencesSearchFilter):

audience_id: Unique identifier for the audience
audience_type: Type of audience
cover_num: Number of audience members covered
create_time: Timestamp indicating when the audience was created
is_valid: Flag indicating if the audience data is valid
name: Name of the audience
shared: Flag indicating if the audience is shared

Returns: AudiencesSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[Audience], AudiencesListResultMeta] : Get custom audiences for an advertiser

Args: advertiser_id: Advertiser ID page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: AudiencesListResult

CampaignsQuery(connector: TiktokMarketingConnector) : Query class for Campaigns entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: CampaignsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CampaignsSearchData] : Search campaigns records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (CampaignsSearchFilter):

advertiser_id: The unique identifier of the advertiser associated with the campaign
app_promotion_type: Type of app promotion being used in the campaign
bid_type: Type of bid strategy being used in the campaign
budget: Total budget allocated for the campaign
budget_mode: Mode in which the budget is being managed (e.g., daily, lifetime)
budget_optimize_on: The metric or event that the budget optimization is based on
campaign_id: The unique identifier of the campaign
campaign_name: Name of the campaign for easy identification
campaign_type: Type of campaign (e.g., awareness, conversion)
create_time: Timestamp when the campaign was created
deep_bid_type: Advanced bid type used for campaign optimization
is_new_structure: Flag indicating if the campaign utilizes a new campaign structure
is_search_campaign: Flag indicating if the campaign is a search campaign
is_smart_performance_campaign: Flag indicating if the campaign uses smart performance optimization
modify_time: Timestamp when the campaign was last modified
objective: The objective or goal of the campaign
objective_type: Type of objective selected for the campaign
operation_status: Current operational status of the campaign
optimization_goal: Specific goal to be optimized for in the campaign
rf_campaign_type: Type of RF (reach and frequency) campaign being run
roas_bid: Return on ad spend goal set for the campaign
secondary_status: Additional status information of the campaign
split_test_variable: Variable being tested in a split test campaign

Returns: CampaignsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[Campaign], CampaignsListResultMeta] : Get campaigns for an advertiser

Args: advertiser_id: Advertiser ID page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: CampaignsListResult

CampaignsReportsDailyQuery(connector: TiktokMarketingConnector) : Query class for CampaignsReportsDaily entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: CampaignsReportsDailySearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CampaignsReportsDailySearchData] : Search campaigns_reports_daily records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (CampaignsReportsDailySearchFilter):

campaign_id: The unique identifier for the campaign.
stat_time_day: The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).
campaign_name: The name of the marketing campaign.
spend: Total amount of money spent.
cpc: Cost per click.
cpm: Cost per thousand impressions.
impressions: Number of times the ad was displayed.
clicks: Number of clicks on the ad.
ctr: Click-through rate.
reach: Total number of unique users reached.
cost_per_1000_reached: Cost per 1000 unique users reached.
frequency: Average number of times each person saw the ad.
video_play_actions: Number of video play actions.
video_watched_2s: Number of times video was watched for at least 2 seconds.
video_watched_6s: Number of times video was watched for at least 6 seconds.
average_video_play: Average video play duration.
average_video_play_per_user: Average video play duration per user.
video_views_p25: Number of times video was watched to 25%.
video_views_p50: Number of times video was watched to 50%.
video_views_p75: Number of times video was watched to 75%.
video_views_p100: Number of times video was watched to 100%.
profile_visits: Number of profile visits.
likes: Number of likes.
comments: Number of comments.
shares: Number of shares.
follows: Number of follows.
clicks_on_music_disc: Number of clicks on the music disc.
real_time_app_install: Real-time app installations.
real_time_app_install_cost: Cost of real-time app installations.
app_install: Number of app installations.

Returns: CampaignsReportsDailySearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, service_type: str, report_type: str, data_level: str, dimensions: str, metrics: str, start_date: str, end_date: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[CampaignsReportDaily], CampaignsReportsDailyListResultMeta] : Get daily performance reports at the campaign level

Returns: CampaignsReportsDailyListResult

CatalogsQuery(connector: TiktokMarketingConnector) : Query class for Catalogs entity operations.

Initialize query with connector reference.

Methods

list(self, advertiser_id: str, bc_id: str | None = None, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[Catalog], CatalogsListResultMeta] : Get product catalogs for an advertiser

Args: advertiser_id: Advertiser ID bc_id: Business Center ID. Required by the TikTok API to scope catalog results. page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: CatalogsListResult

CreativeAssetsImagesQuery(connector: TiktokMarketingConnector) : Query class for CreativeAssetsImages entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: CreativeAssetsImagesSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CreativeAssetsImagesSearchData] : Search creative_assets_images records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (CreativeAssetsImagesSearchFilter):

create_time: The timestamp when the image was created.
file_name: The name of the image file.
format: The format type of the image file.
height: The height dimension of the image.
image_id: The unique identifier for the image.
image_url: The URL to access the image.
modify_time: The timestamp when the image was last modified.
size: The size of the image file.
width: The width dimension of the image.

Returns: CreativeAssetsImagesSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[CreativeAssetImage], CreativeAssetsImagesListResultMeta] : Search creative asset images for an advertiser

Args: advertiser_id: Advertiser ID page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: CreativeAssetsImagesListResult

CreativeAssetsVideosQuery(connector: TiktokMarketingConnector) : Query class for CreativeAssetsVideos entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: CreativeAssetsVideosSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CreativeAssetsVideosSearchData] : Search creative_assets_videos records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (CreativeAssetsVideosSearchFilter):

create_time: Timestamp when the video was created.
duration: Duration of the video in seconds.
file_name: Name of the video file.
format: Format of the video file.
height: Height of the video in pixels.
modify_time: Timestamp when the video was last modified.
size: Size of the video file in bytes.
video_cover_url: URL for the cover image of the video.
video_id: ID of the video.
width: Width of the video in pixels.

Returns: CreativeAssetsVideosSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[CreativeAssetVideo], CreativeAssetsVideosListResultMeta] : Search creative asset videos for an advertiser

Args: advertiser_id: Advertiser ID page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: CreativeAssetsVideosListResult

SparkAdsQuery(connector: TiktokMarketingConnector) : Query class for SparkAds entity operations.

Initialize query with connector reference.

Methods

context_store_search(self, query: SparkAdsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[SparkAdsSearchData] : Search spark_ads records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (SparkAdsSearchFilter):

item_info: Information about the Spark Ads post including item_id, auth_code, text, status, and item_type.
user_info: Information about the TikTok account including tiktok_name, identity_id, and identity_type.
auth_info: Authorization details including invite_start_time, auth_start_time, auth_end_time, and ad_auth_status.
video_info: Video post details including duration, preview_url, poster_url, height, width, and size.

Returns: SparkAdsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, advertiser_id: str, page: int | None = None, page_size: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingExecuteResultWithMeta[list[SparkAd], SparkAdsListResultMeta] : Get Spark Ad posts that have been authorized to an ad account

Args: advertiser_id: Advertiser ID page: Page number page_size: Number of items per page **kwargs: Additional parameters

Returns: SparkAdsListResult

TiktokMarketingConnector(auth_config: TiktokMarketingAuthConfig | AirbyteAuthConfig | BaseModel | None = None, on_token_refresh: Any | None = None) : Type-safe Tiktok-Marketing API connector.

Auto-generated from OpenAPI specification with full type safety.

Initialize a new tiktok-marketing connector instance.

Supports both local and hosted execution modes:

Local mode: Provide connector-specific auth config (e.g., TiktokMarketingAuthConfig)
Hosted mode: Provide AirbyteAuthConfig with client credentials and either connector_id or workspace_name

Args: auth_config: Either connector-specific auth config for local mode, or AirbyteAuthConfig for hosted mode on_token_refresh: Optional callback for OAuth2 token refresh persistence. Called with new_tokens dict when tokens are refreshed. Can be sync or async. Example: lambda tokens: save_to_database(tokens) Examples:

Local mode (direct API calls)

connector = TiktokMarketingConnector(auth_config=TiktokMarketingAuthConfig(access_token="..."))

Hosted mode with explicit connector_id (no lookup needed)

connector = TiktokMarketingConnector( auth_config=AirbyteAuthConfig( airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789", connector_id="existing-source-uuid" ) )

Hosted mode with lookup by workspace_name

connector = TiktokMarketingConnector( auth_config=AirbyteAuthConfig( workspace_name="user-123", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789" ) )

Class variables

connector_name : The type of the None singleton.

connector_version : The type of the None singleton.

sdk_version : The type of the None singleton.

Static methods

agent_tool(role: AgentToolRole | None = None, *, inspect_tool: str | None = None, docs_tool: str | None = None, max_output_chars: int | None | Unset = UNSET, framework: FrameworkName = 'none', internal_retries: int = 0, should_internal_retry: Callable[[Exception, tuple[Any, ...], dict[str, Any]], bool] | None = None, exhausted_runtime_failure_message: Callable[[Exception, tuple[Any, ...], dict[str, Any]], str | None] | None = None) ‑> Callable[[~_F], ~_F] : Decorator for new user-written connector tool functions.

Use this when a tool needs a custom body or the framework lacks a native strategy. Instead of baking the full entity/action reference into the docstring, it instructs the agent to call this connector's inspect and docs tools before executing. Tool failures raise :class:airbyte_agent_sdk.AirbyteToolError by default (framework="none", no auto-detection) — pass framework=... to translate to a supported framework's signal instead.

Decorate three functions per connector — execute, inspect and docs. The role is inferred from each function's signature (extra parameters are allowed); a signature matching more than one role, a generic (*args, **kwargs) wrapper, or a callable whose signature cannot be read must pass the role explicitly:

(entity, action, ...) -> "execute"
(section, ...) -> "read_skill_docs"
() -> "inspect_connector"

Usage: connector = TiktokMarketingConnector(...)

@TiktokMarketingConnector.agent_tool() async def execute(entity: str, action: str, params: dict | None = None): return await connector.execute(entity=entity, action=action, params=params or {})

@TiktokMarketingConnector.agent_tool() async def inspect_connector(): return await connector.inspect_connector()

@TiktokMarketingConnector.agent_tool() async def read_skill_docs(section: str | None = None): return await connector.read_skill_docs(section)

Args: role: "execute" | "inspect_connector" | "read_skill_docs". None (default) infers the role from the decorated function's signature; an explicit role validates the canonical parameters are present (functions accepting **kwargs, or callables whose signature cannot be read, pass validation). inspect_tool: Exact registered name of the sibling inspect tool, woven into the execute docstring for tighter steering. Defaults to generic phrasing. docs_tool: Exact registered name of the sibling docs tool (see inspect_tool). max_output_chars: Max serialized output size before failing. Defaults per role: execute -> DEFAULT_MAX_OUTPUT_CHARS, docs tools -> None. framework: Translation target for tool failures. Defaults to "none" (raise AirbyteToolError); never auto-detects. internal_retries: How many transient runtime failures (429/5xx, network, timeout) to retry silently before surfacing. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions. should_internal_retry: Optional predicate (error, args, kwargs) -> bool further restricting which retryable errors are safe for this specific tool. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions. exhausted_runtime_failure_message: Optional callback (error, args, kwargs) -> str | None invoked after internal retries are exhausted or skipped. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions.

tool_utils(func: _F | None = None, *, update_docstring: bool = True, max_output_chars: int | None = 100000, framework: FrameworkName | None = None, internal_retries: int = 0, should_internal_retry: Callable[[Exception, tuple[Any, ...], dict[str, Any]], bool] | None = None, exhausted_runtime_failure_message: Callable[[Exception, tuple[Any, ...], dict[str, Any]], str | None] | None = None) ‑> ~_F | Callable[[~_F], ~_F] : Deprecated. Add connector-specific documentation and runtime safeguards to one tool.

Kept for backwards compatibility with existing single-tool integrations; it is not removed and does not warn at runtime, but new code should use build_connector_tools or agent_tool below.

For new agents, prefer build_connector_tools. It returns progressive inspect_connector, read_skill_docs, and execute tools so the agent can load only the connector guidance it needs:

from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

When a new integration needs custom tool bodies or a framework without native support, use agent_tool instead.

Legacy: one generated-description tool

Existing integrations can keep using tool_utils for one broad execute tool with the connector's full generated catalog in its description:

from fastmcp import FastMCP

connector = TiktokMarketingConnector()
mcp = FastMCP("Connector Agent")

@mcp.tool()
@TiktokMarketingConnector.tool_utils
async def execute(entity: str, action: str, params: dict):
    ...

Configure documentation, output limits, framework translation, and retries when needed:

@mcp.tool()
@TiktokMarketingConnector.tool_utils(update_docstring=False, max_output_chars=None)
async def execute(entity: str, action: str, params: dict):
    ...

@mcp.tool()
@TiktokMarketingConnector.tool_utils(framework="pydantic_ai", internal_retries=2)
async def execute(entity: str, action: str, params: dict):
    ...

This decorator composes translate_exceptions for runtime wrapping, output-size checks, framework signal translation, and optional internal retries, then adds connector-specific docstring augmentation.

Args: update_docstring: When True, append connector capabilities to __doc__. max_output_chars: Max serialized output size before raising. Use None to disable. framework: One of "pydantic_ai" | "langchain" | "openai_agents" | "mcp" | "none". Defaults to None, which auto-detects each framework's canonical import in order and falls back to "none" with a warning when no supported framework is installed. Explicit always wins, and an explicit framework whose package is missing raises RuntimeError. internal_retries: How many transient runtime failures (429/5xx, network, timeout) to retry silently before surfacing. Default 0. Forwarded to airbyte_agent_sdk.translation.translate_exceptions. should_internal_retry: Optional predicate (error, args, kwargs) -> bool further restricting which retryable errors are safe for this specific tool. Forwarded to airbyte_agent_sdk.translation.translate_exceptions. exhausted_runtime_failure_message: Optional callback (error, args, kwargs) -> str | None. Invoked after internal retries are exhausted or were skipped because should_internal_retry returned False. Forwarded to airbyte_agent_sdk.translation.translate_exceptions.

Instance variables

connector_id: str | None : Get the connector/source ID (only available in hosted mode).

Returns: The connector ID if in hosted mode, None if in local mode.

Methods

check(self) ‑> airbyte_agent_sdk.connectors.tiktok_marketing.models.TiktokMarketingCheckResult : Perform a health check to verify connectivity and credentials.

Executes a lightweight list operation (limit=1) to validate that the connector can communicate with the API and credentials are valid.

Returns: TiktokMarketingCheckResult with status ("healthy" or "unhealthy") and optional error message

Example: result = await connector.check() if result.status == "healthy": print("Connection verified!") else: print(f"Check failed: {result.error}")

close(self) : Close the connector and release resources.

entity_schema(self, entity: str) ‑> dict[str, typing.Any] | None : Get the JSON schema for an entity.

Args: entity: Entity name (e.g., "contacts", "companies")

Returns: JSON schema dict describing the entity structure, or None if not found.

Example: schema = connector.entity_schema("contacts") if schema: print(f"Contact properties: {list(schema.get('properties', {}).keys())}")

execute(self, entity: str, action: "Literal['list', 'context_store_search', 'context_store_sql_query']", params: Mapping[str, Any] | None = None, *, select_fields: list[str] | None = None, exclude_fields: list[str] | None = None, skip_truncation: bool = True) ‑> Any : Execute an entity operation with full type safety.

This is the recommended interface for blessed connectors as it:

Uses the same signature as non-blessed connectors
Provides full IDE autocomplete for entity/action/params
Makes migration from generic to blessed connectors seamless

Args: entity: Entity name (e.g., "customers") action: Operation action (e.g., "create", "get", "list") params: Operation parameters (typed based on entity+action) select_fields: Optional allowlist of dot-notation fields to include exclude_fields: Optional blocklist of dot-notation fields to remove skip_truncation: Disable long-text truncation for collection actions

Returns: Typed response based on the operation

Example: customer = await connector.execute( entity="customers", action="get", params={"id": "cus_123"} )

inspect_connector(self) ‑> dict[str, typing.Any] : Inspect this connector's hosted metadata/readiness and resolve its docs skill id.

Call this before read_skill_docs in the normal hosted flow. For local/offline connectors this returns a local-mode payload with a warning instead of a hosted inspection.

Example: info = await connector.inspect_connector() print(info["docs_skill_id"])

list_entities(self) ‑> list[dict[str, typing.Any]] : Get structured data about available entities, actions, and parameters.

Returns a list of entity descriptions with:

entity_name: Name of the entity (e.g., "contacts", "deals")
description: Entity description from the first endpoint
available_actions: List of actions (e.g., ["list", "get", "create"])
parameters: Dict mapping action -> list of parameter dicts

Example: entities = connector.list_entities() for entity in entities: print(f"{entity['entity_name']}: {entity['available_actions']}")

read_skill_docs(self, section: str | None = None) ‑> str : Read this connector's usage docs, rendered to text.

Omit section for the outline and general guidance; pass an exact section id from the outline for full details. For local/offline connectors the full generated docs are returned and section is ignored.

Example: outline = await connector.read_skill_docs() details = await connector.read_skill_docs(section="entity:contacts")

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Local mode (direct API calls)

Hosted mode with explicit connector_id (no lookup needed)

Hosted mode with lookup by workspace_name

Class variables​

Static methods​

Legacy: one generated-description tool​

Instance variables​

Methods​

Classes

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Class variables

Static methods

Legacy: one generated-description tool

Instance variables

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