Module airbyte_agent_sdk.connectors.tiktok_marketing

Tiktok-Marketing connector for Airbyte SDK.

Auto-generated from OpenAPI specification.

Sub-modules

• airbyte_agent_sdk.connectors.tiktok_marketing.connector
• airbyte_agent_sdk.connectors.tiktok_marketing.connector_model
• airbyte_agent_sdk.connectors.tiktok_marketing.models
• airbyte_agent_sdk.connectors.tiktok_marketing.types

Classes

AdGroupsReportsDailySearchData(**data: Any) : Search result data for ad_groups_reports_daily entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

adgroup_id: int | None : The unique identifier for the ad group.

adgroup_name: str | None : The name of the ad group.

app_install: float | None : Number of app installations.

average_video_play: float | None : Average video play duration.

average_video_play_per_user: float | None : Average video play duration per user.

campaign_id: int | None : The unique identifier for the campaign.

campaign_name: str | None : The name of the marketing campaign.

clicks: str | None : Number of clicks on the ad.

clicks_on_music_disc: float | None : Number of clicks on the music disc.

comments: float | None : Number of comments.

conversion: str | None : Number of conversions.

conversion_rate: str | None : Rate of conversions.

cost_per_1000_reached: str | None : Cost per 1000 unique users reached.

cost_per_conversion: str | None : Cost per conversion.

cost_per_result: str | None : Cost per result.

cost_per_secondary_goal_result: str | None : Cost per secondary goal result.

cpc: str | None : Cost per click.

cpm: str | None : Cost per thousand impressions.

ctr: str | None : Click-through rate.

follows: float | None : Number of follows.

frequency: str | None : Average number of times each person saw the ad.

impressions: str | None : Number of times the ad was displayed.

likes: float | None : Number of likes.

model_config : The type of the None singleton.

placement_type: str | None : Type of ad placement.

profile_visits: float | None : Number of profile visits.

reach: str | None : Total number of unique users reached.

real_time_app_install: float | None : Real-time app installations.

real_time_app_install_cost: float | None : Cost of real-time app installations.

real_time_conversion: str | None : Real-time conversions.

real_time_conversion_rate: str | None : Real-time conversion rate.

real_time_cost_per_conversion: str | None : Real-time cost per conversion.

real_time_cost_per_result: str | None : Real-time cost per result.

real_time_result: str | None : Real-time results.

real_time_result_rate: str | None : Real-time result rate.

result: str | None : Number of results.

result_rate: str | None : Rate of results.

secondary_goal_result: str | None : Results for secondary goals.

secondary_goal_result_rate: str | None : Rate of secondary goal results.

shares: float | None : Number of shares.

spend: str | None : Total amount of money spent.

stat_time_day: str | None : The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).

video_play_actions: float | None : Number of video play actions.

video_views_p100: float | None : Number of times video was watched to 100%.

video_views_p25: float | None : Number of times video was watched to 25%.

video_views_p50: float | None : Number of times video was watched to 50%.

video_views_p75: float | None : Number of times video was watched to 75%.

video_watched_2s: float | None : Number of times video was watched for at least 2 seconds.

video_watched_6s: float | None : Number of times video was watched for at least 6 seconds.

AdGroupsSearchData(**data: Any) : Search result data for ad_groups entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

adgroup_id: int | None : The unique identifier of the ad group

adgroup_name: str | None : The name of the ad group

advertiser_id: int | None : The unique identifier of the advertiser

budget: float | None : The allocated budget for the ad group

budget_mode: str | None : The mode for managing the budget

campaign_id: int | None : The unique identifier of the campaign

create_time: str | None : The timestamp for when the ad group was created

model_config : The type of the None singleton.

modify_time: str | None : The timestamp for when the ad group was last modified

operation_status: str | None : The status of the operation

optimization_goal: str | None : The goal set for optimization

placement_type: str | None : The type of ad placement

promotion_type: str | None : The type of promotion

secondary_status: str | None : The secondary status of the ad group

AdsReportsDailySearchData(**data: Any) : Search result data for ads_reports_daily entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

ad_id: int | None : The unique identifier for the ad.

ad_name: str | None : The name of the ad.

ad_text: str | None : The text content of the ad.

adgroup_id: int | None : The unique identifier for the ad group.

adgroup_name: str | None : The name of the ad group.

app_install: float | None : Number of app installations.

average_video_play: float | None : Average video play duration.

average_video_play_per_user: float | None : Average video play duration per user.

campaign_id: int | None : The unique identifier for the campaign.

campaign_name: str | None : The name of the marketing campaign.

clicks: str | None : Number of clicks on the ad.

clicks_on_music_disc: float | None : Number of clicks on the music disc.

comments: float | None : Number of comments.

conversion: str | None : Number of conversions.

conversion_rate: str | None : Rate of conversions.

cost_per_1000_reached: str | None : Cost per 1000 unique users reached.

cost_per_conversion: str | None : Cost per conversion.

cost_per_result: str | None : Cost per result.

cost_per_secondary_goal_result: str | None : Cost per secondary goal result.

cpc: str | None : Cost per click.

cpm: str | None : Cost per thousand impressions.

ctr: str | None : Click-through rate.

follows: float | None : Number of follows.

frequency: str | None : Average number of times each person saw the ad.

impressions: str | None : Number of times the ad was displayed.

likes: float | None : Number of likes.

model_config : The type of the None singleton.

placement_type: str | None : Type of ad placement.

profile_visits: float | None : Number of profile visits.

reach: str | None : Total number of unique users reached.

real_time_app_install: float | None : Real-time app installations.

real_time_app_install_cost: float | None : Cost of real-time app installations.

real_time_conversion: str | None : Real-time conversions.

real_time_conversion_rate: str | None : Real-time conversion rate.

real_time_cost_per_conversion: str | None : Real-time cost per conversion.

real_time_cost_per_result: str | None : Real-time cost per result.

real_time_result: str | None : Real-time results.

real_time_result_rate: str | None : Real-time result rate.

result: str | None : Number of results.

result_rate: str | None : Rate of results.

secondary_goal_result: str | None : Results for secondary goals.

secondary_goal_result_rate: str | None : Rate of secondary goal results.

shares: float | None : Number of shares.

spend: str | None : Total amount of money spent.

stat_time_day: str | None : The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).

video_play_actions: float | None : Number of video play actions.

video_views_p100: float | None : Number of times video was watched to 100%.

video_views_p25: float | None : Number of times video was watched to 25%.

video_views_p50: float | None : Number of times video was watched to 50%.

video_views_p75: float | None : Number of times video was watched to 75%.

video_watched_2s: float | None : Number of times video was watched for at least 2 seconds.

video_watched_6s: float | None : Number of times video was watched for at least 6 seconds.

AdsSearchData(**data: Any) : Search result data for ads entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

ad_format: str | None : The format of the ad

ad_id: int | None : The unique identifier of the ad

ad_name: str | None : The name of the ad

ad_text: str | None : The text content of the ad

adgroup_id: int | None : The unique identifier of the ad group

adgroup_name: str | None : The name of the ad group

advertiser_id: int | None : The unique identifier of the advertiser

campaign_id: int | None : The unique identifier of the campaign

campaign_name: str | None : The name of the campaign

create_time: str | None : The timestamp when the ad was created

landing_page_url: str | None : The URL of the landing page for the ad

model_config : The type of the None singleton.

modify_time: str | None : The timestamp when the ad was last modified

operation_status: str | None : The operational status of the ad

secondary_status: str | None : The secondary status of the ad

video_id: str | None : The unique identifier of the video

AdvertisersReportsDailySearchData(**data: Any) : Search result data for advertisers_reports_daily entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

advertiser_id: int | None : The unique identifier for the advertiser.

app_install: float | None : Number of app installations.

average_video_play: float | None : Average video play duration.

average_video_play_per_user: float | None : Average video play duration per user.

cash_spend: str | None : The amount of money spent in cash.

clicks: str | None : Number of clicks on the ad.

clicks_on_music_disc: float | None : Number of clicks on the music disc.

comments: float | None : Number of comments.

cost_per_1000_reached: str | None : Cost per 1000 unique users reached.

cpc: str | None : Cost per click.

cpm: str | None : Cost per thousand impressions.

ctr: str | None : Click-through rate.

follows: float | None : Number of follows.

frequency: str | None : Average number of times each person saw the ad.

impressions: str | None : Number of times the ad was displayed.

likes: float | None : Number of likes.

model_config : The type of the None singleton.

profile_visits: float | None : Number of profile visits.

reach: str | None : Total number of unique users reached.

real_time_app_install: float | None : Real-time app installations.

real_time_app_install_cost: float | None : Cost of real-time app installations.

shares: float | None : Number of shares.

spend: str | None : Total amount of money spent.

stat_time_day: str | None : The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).

video_play_actions: float | None : Number of video play actions.

video_views_p100: float | None : Number of times video was watched to 100%.

video_views_p25: float | None : Number of times video was watched to 25%.

video_views_p50: float | None : Number of times video was watched to 50%.

video_views_p75: float | None : Number of times video was watched to 75%.

video_watched_2s: float | None : Number of times video was watched for at least 2 seconds.

video_watched_6s: float | None : Number of times video was watched for at least 6 seconds.

voucher_spend: str | None : Amount spent using vouchers.

AdvertisersSearchData(**data: Any) : Search result data for advertisers entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

address: str | None : The physical address of the advertiser.

advertiser_account_type: str | None : The type of advertiser's account (e.g., individual, business).

advertiser_id: int | None : Unique identifier for the advertiser.

balance: float | None : The current balance in the advertiser's account.

brand: str | None : The brand name associated with the advertiser.

cellphone_number: str | None : The cellphone number of the advertiser.

company: str | None : The name of the company associated with the advertiser.

contacter: str | None : The contact person for the advertiser.

country: str | None : The country where the advertiser is located.

create_time: int | None : The timestamp when the advertiser account was created.

currency: str | None : The currency used for transactions in the account.

description: str | None : A brief description or bio of the advertiser or company.

display_timezone: str | None : The timezone for display purposes.

email: str | None : The email address associated with the advertiser.

industry: str | None : The industry or sector the advertiser operates in.

language: str | None : The preferred language of communication for the advertiser.

license_city: str | None : The city where the advertiser's license is registered.

license_no: str | None : The license number of the advertiser.

license_province: str | None : The province or state where the advertiser's license is registered.

license_url: str | None : The URL link to the advertiser's license documentation.

model_config : The type of the None singleton.

name: str | None : The name of the advertiser or company.

promotion_area: str | None : The specific area or region where the advertiser focuses promotion.

promotion_center_city: str | None : The city at the center of the advertiser's promotion activities.

promotion_center_province: str | None : The province or state at the center of the advertiser's promotion activities.

rejection_reason: str | None : Reason for any advertisement rejection by the platform.

role: str | None : The role or position of the advertiser within the company.

status: str | None : The current status of the advertiser's account.

telephone_number: str | None : The telephone number of the advertiser.

timezone: str | None : The timezone setting for the advertiser's activities.

AirbyteAuthConfig(**data: Any) : Authentication configuration for Airbyte hosted mode execution.

Pass this to the connector's auth_config parameter to use hosted mode, where API credentials are stored securely in Airbyte Cloud.

For hosted mode execution, provide client credentials with either:

• connector_id: Direct connector/source ID (skips lookup)
• workspace_name: Workspace name for connector lookup

Attributes: workspace_name: Workspace name for hosted mode connector lookup organization_id: Optional Airbyte organization ID for multi-org selection airbyte_client_id: Airbyte OAuth client ID (required for hosted mode) airbyte_client_secret: Airbyte OAuth client secret (required for hosted mode) connector_id: Specific connector/source ID (skips lookup if provided)

Examples:

Hosted mode with connector_id (no lookup needed)

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

Hosted mode with workspace_name (lookup by workspace)

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

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

airbyte_client_id: str | None : The type of the None singleton.

airbyte_client_secret: str | None : The type of the None singleton.

connector_id: str | None : The type of the None singleton.

model_config : The type of the None singleton.

organization_id: str | None : The type of the None singleton.

workspace_name: str | None : The type of the None singleton.

AirbyteSearchMeta(**data: Any) : Pagination metadata for search responses.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

cursor: str | None : Cursor for fetching the next page of results.

has_more: bool : Whether more results are available.

model_config : The type of the None singleton.

took_ms: int | None : Time taken to execute the search in milliseconds.

AirbyteSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel
• typing.Generic

Descendants

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdGroupsReportsDailySearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdGroupsSearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdsReportsDailySearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdsSearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdvertisersReportsDailySearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AdvertisersSearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[AudiencesSearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CampaignsReportsDailySearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CampaignsSearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CreativeAssetsImagesSearchData]
• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult[CreativeAssetsVideosSearchData]

Class variables

data: list[~D] : List of matching records.

meta: airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchMeta : Pagination metadata.

model_config : The type of the None singleton.

AdGroupsReportsDailySearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

AdGroupsSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

AdsReportsDailySearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

AdsSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

AdvertisersReportsDailySearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

AdvertisersSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

AudiencesSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

CampaignsReportsDailySearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

CampaignsSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

CreativeAssetsImagesSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

CreativeAssetsVideosSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.tiktok_marketing.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

AudiencesSearchData(**data: Any) : Search result data for audiences entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

audience_id: str | None : Unique identifier for the audience

audience_type: str | None : Type of audience

cover_num: int | None : Number of audience members covered

create_time: str | None : Timestamp indicating when the audience was created

is_valid: bool | None : Flag indicating if the audience data is valid

model_config : The type of the None singleton.

name: str | None : Name of the audience

shared: bool | None : Flag indicating if the audience is shared

CampaignsReportsDailySearchData(**data: Any) : Search result data for campaigns_reports_daily entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

app_install: float | None : Number of app installations.

average_video_play: float | None : Average video play duration.

average_video_play_per_user: float | None : Average video play duration per user.

campaign_id: int | None : The unique identifier for the campaign.

campaign_name: str | None : The name of the marketing campaign.

clicks: str | None : Number of clicks on the ad.

clicks_on_music_disc: float | None : Number of clicks on the music disc.

comments: float | None : Number of comments.

cost_per_1000_reached: str | None : Cost per 1000 unique users reached.

cpc: str | None : Cost per click.

cpm: str | None : Cost per thousand impressions.

ctr: str | None : Click-through rate.

follows: float | None : Number of follows.

frequency: str | None : Average number of times each person saw the ad.

impressions: str | None : Number of times the ad was displayed.

likes: float | None : Number of likes.

model_config : The type of the None singleton.

profile_visits: float | None : Number of profile visits.

reach: str | None : Total number of unique users reached.

real_time_app_install: float | None : Real-time app installations.

real_time_app_install_cost: float | None : Cost of real-time app installations.

shares: float | None : Number of shares.

spend: str | None : Total amount of money spent.

stat_time_day: str | None : The date for which the statistical data is recorded (YYYY-MM-DD HH:MM:SS format).

video_play_actions: float | None : Number of video play actions.

video_views_p100: float | None : Number of times video was watched to 100%.

video_views_p25: float | None : Number of times video was watched to 25%.

video_views_p50: float | None : Number of times video was watched to 50%.

video_views_p75: float | None : Number of times video was watched to 75%.

video_watched_2s: float | None : Number of times video was watched for at least 2 seconds.

video_watched_6s: float | None : Number of times video was watched for at least 6 seconds.

CampaignsSearchData(**data: Any) : Search result data for campaigns entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

advertiser_id: int | None : The unique identifier of the advertiser associated with the campaign

app_promotion_type: str | None : Type of app promotion being used in the campaign

bid_type: str | None : Type of bid strategy being used in the campaign

budget: float | None : Total budget allocated for the campaign

budget_mode: str | None : Mode in which the budget is being managed (e.g., daily, lifetime)

budget_optimize_on: bool | None : The metric or event that the budget optimization is based on

campaign_id: int | None : The unique identifier of the campaign

campaign_name: str | None : Name of the campaign for easy identification

campaign_type: str | None : Type of campaign (e.g., awareness, conversion)

create_time: str | None : Timestamp when the campaign was created

deep_bid_type: str | None : Advanced bid type used for campaign optimization

is_new_structure: bool | None : Flag indicating if the campaign utilizes a new campaign structure

is_search_campaign: bool | None : Flag indicating if the campaign is a search campaign

is_smart_performance_campaign: bool | None : Flag indicating if the campaign uses smart performance optimization

model_config : The type of the None singleton.

modify_time: str | None : Timestamp when the campaign was last modified

objective: str | None : The objective or goal of the campaign

objective_type: str | None : Type of objective selected for the campaign

operation_status: str | None : Current operational status of the campaign

optimization_goal: str | None : Specific goal to be optimized for in the campaign

rf_campaign_type: str | None : Type of RF (reach and frequency) campaign being run

roas_bid: float | None : Return on ad spend goal set for the campaign

secondary_status: str | None : Additional status information of the campaign

split_test_variable: str | None : Variable being tested in a split test campaign

CreativeAssetsImagesSearchData(**data: Any) : Search result data for creative_assets_images entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

create_time: str | None : The timestamp when the image was created.

file_name: str | None : The name of the image file.

format: str | None : The format type of the image file.

height: int | None : The height dimension of the image.

image_id: str | None : The unique identifier for the image.

image_url: str | None : The URL to access the image.

model_config : The type of the None singleton.

modify_time: str | None : The timestamp when the image was last modified.

size: int | None : The size of the image file.

width: int | None : The width dimension of the image.

CreativeAssetsVideosSearchData(**data: Any) : Search result data for creative_assets_videos entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

create_time: str | None : Timestamp when the video was created.

duration: float | None : Duration of the video in seconds.

file_name: str | None : Name of the video file.

format: str | None : Format of the video file.

height: int | None : Height of the video in pixels.

model_config : The type of the None singleton.

modify_time: str | None : Timestamp when the video was last modified.

size: int | None : Size of the video file in bytes.

video_cover_url: str | None : URL for the cover image of the video.

video_id: str | None : ID of the video.

width: int | None : Width of the video in pixels.

TiktokMarketingAuthConfig(**data: Any) : OAuth Access Token

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

access_token: str : Your TikTok Marketing API access token

model_config : The type of the None singleton.

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

create(*, airbyte_config: AirbyteAuthConfig, auth_config: "'TiktokMarketingAuthConfig'", name: str | None = None, replication_config: "'TiktokMarketingReplicationConfig' | None" = None, source_template_id: str | None = None) : Create a new hosted connector on Airbyte Cloud.

This factory method:

1. Creates a source on Airbyte Cloud with the provided credentials
2. Returns a connector configured with the new connector_id

Args: airbyte_config: Airbyte hosted auth config with client credentials and workspace_name. Optionally include organization_id for multi-org request routing. auth_config: Typed auth config (same as local mode) name: Optional source name (defaults to connector name + workspace_name) replication_config: Typed replication settings. Required for connectors with x-airbyte-replication-config (REPLICATION mode sources). source_template_id: Source template ID. Required when organization has multiple source templates for this connector type.

Returns: A TiktokMarketingConnector instance configured in hosted mode

Example:

Create a new hosted connector with API key auth

connector = await TiktokMarketingConnector.create( airbyte_config=AirbyteAuthConfig( workspace_name="my-workspace", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc", airbyte_client_secret="secret_xyz", ), auth_config=TiktokMarketingAuthConfig(access_token="..."), )

With replication config (required for this connector):

connector = await TiktokMarketingConnector.create( airbyte_config=AirbyteAuthConfig( workspace_name="my-workspace", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc", airbyte_client_secret="secret_xyz", ), auth_config=TiktokMarketingAuthConfig(access_token="..."), replication_config=TiktokMarketingReplicationConfig(start_date="..."), )

Use the connector

result = await connector.execute("entity", "list", {})

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] : Decorator that adds tool utilities like docstring augmentation and output limits.

Composes :func:airbyte_agent_sdk.translation.translate_exceptions for runtime wrapping (sync/async branch + output-size check + framework signal translation + optional internal retry loop), and adds connector-specific docstring augmentation on top of it.

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

@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): ...

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". Defaults to None → auto-detect by attempting each framework's canonical import in order. Explicit always wins. internal_retries: How many transient runtime failures (429/5xx, network, timeout) to retry silently before surfacing. Default 0. 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 were skipped via should_internal_retry returning False. Forwarded to :func: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.

Example: connector = await TiktokMarketingConnector.create(...) print(f"Created connector: {connector.connector_id}")

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']", params: Mapping[str, Any] | None = None) ‑> 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)

Returns: Typed response based on the operation

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

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']}")

TiktokMarketingReplicationConfig(**data: Any) : Replication Configuration - Settings for data replication from TikTok Marketing.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

model_config : The type of the None singleton.

start_date: str : The start date in YYYY-MM-DD format. Any data before this date will not be replicated. If not set, defaults to 2016-09-01.