Module airbyte_agent_sdk.connectors.linkedin_ads

Linkedin-Ads connector for Airbyte SDK.

Auto-generated from OpenAPI specification.

Sub-modules

airbyte_agent_sdk.connectors.linkedin_ads.connector
airbyte_agent_sdk.connectors.linkedin_ads.connector_model
airbyte_agent_sdk.connectors.linkedin_ads.models
airbyte_agent_sdk.connectors.linkedin_ads.types

Classes

AccountUsersSearchData(**data: Any) : Search result data for account_users 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

account: str | None : Associated account URN

model_config : The type of the None singleton.

role: str | None : User role in the account

user: str | None : User URN

AccountsSearchData(**data: Any) : Search result data for accounts 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

currency: str | None : Currency code used by the account

id: int | None : Unique account identifier

model_config : The type of the None singleton.

name: str | None : Account name

notified_on_campaign_optimization: bool | None : Flag for notifications on campaign optimization

notified_on_creative_approval: bool | None : Flag for notifications on creative approval

notified_on_creative_rejection: bool | None : Flag for notifications on creative rejection

notified_on_end_of_campaign: bool | None : Flag for notifications on end of campaign

notified_on_new_features_enabled: bool | None : Flag for notifications on new features

reference: str | None : Reference organization URN

serving_statuses: list[typing.Any] | None : List of serving statuses

status: str | None : Account status

test: bool | None : Whether this is a test account

type_: str | None : Account type

version: dict[str, typing.Any] | None : Version information

AdCampaignAnalyticsSearchData(**data: Any) : Search result data for ad_campaign_analytics 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

action_clicks: float | None : Number of action clicks

ad_unit_clicks: float | None : Number of ad unit clicks

approximate_member_reach: float | None : Approximate unique member reach

card_clicks: float | None : Number of carousel card clicks

card_impressions: float | None : Number of carousel card impressions

clicks: float | None : Number of clicks on the ad

comment_likes: float | None : Number of comment likes

comments: float | None : Number of comments

company_page_clicks: float | None : Number of company page clicks

conversion_value_in_local_currency: float | None : Conversion value in local currency

cost_in_local_currency: float | None : Total cost in the accounts local currency

cost_in_usd: float | None : Total cost in USD

download_clicks: float | None : Number of download clicks

end_date: str | None : End date of the ad analytics data

external_website_conversions: float | None : Number of conversions on external websites

external_website_post_click_conversions: float | None : Post-click conversions on external websites

external_website_post_view_conversions: float | None : Post-view conversions on external websites

follows: float | None : Number of follows

full_screen_plays: float | None : Number of full screen video plays

impressions: float | None : Number of times the ad was shown

landing_page_clicks: float | None : Number of landing page clicks

likes: float | None : Number of likes

model_config : The type of the None singleton.

one_click_lead_form_opens: float | None : Number of one-click lead form opens

one_click_leads: float | None : Number of one-click leads

opens: float | None : Number of opens (InMail)

other_engagements: float | None : Number of other engagements

pivot_values: list[typing.Any] | None : Pivot values (URNs) for this analytics record

reactions: float | None : Number of reactions

sends: float | None : Number of sends (InMail)

shares: float | None : Number of shares

start_date: str | None : Start date of the ad analytics data

text_url_clicks: float | None : Number of text URL clicks

total_engagements: float | None : Total number of engagements

video_completions: float | None : Number of times video played to 100%

video_first_quartile_completions: float | None : Number of times video played to 25%

video_midpoint_completions: float | None : Number of times video played to 50%

video_starts: float | None : Number of video starts

video_third_quartile_completions: float | None : Number of times video played to 75%

video_views: float | None : Number of video views

AdCreativeAnalyticsSearchData(**data: Any) : Search result data for ad_creative_analytics 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

action_clicks: float | None : Number of action clicks

ad_unit_clicks: float | None : Number of ad unit clicks

approximate_member_reach: float | None : Approximate unique member reach

card_clicks: float | None : Number of carousel card clicks

card_impressions: float | None : Number of carousel card impressions

clicks: float | None : Number of clicks on the ad

comment_likes: float | None : Number of comment likes

comments: float | None : Number of comments

company_page_clicks: float | None : Number of company page clicks

conversion_value_in_local_currency: float | None : Conversion value in local currency

cost_in_local_currency: float | None : Total cost in the accounts local currency

cost_in_usd: float | None : Total cost in USD

download_clicks: float | None : Number of download clicks

end_date: str | None : End date of the ad analytics data

external_website_conversions: float | None : Number of conversions on external websites

external_website_post_click_conversions: float | None : Post-click conversions on external websites

external_website_post_view_conversions: float | None : Post-view conversions on external websites

follows: float | None : Number of follows

full_screen_plays: float | None : Number of full screen video plays

impressions: float | None : Number of times the ad was shown

landing_page_clicks: float | None : Number of landing page clicks

likes: float | None : Number of likes

model_config : The type of the None singleton.

one_click_lead_form_opens: float | None : Number of one-click lead form opens

one_click_leads: float | None : Number of one-click leads

opens: float | None : Number of opens (InMail)

other_engagements: float | None : Number of other engagements

pivot_values: list[typing.Any] | None : Pivot values (URNs) for this analytics record

reactions: float | None : Number of reactions

sends: float | None : Number of sends (InMail)

shares: float | None : Number of shares

start_date: str | None : Start date of the ad analytics data

text_url_clicks: float | None : Number of text URL clicks

total_engagements: float | None : Total number of engagements

video_completions: float | None : Number of times video played to 100%

video_first_quartile_completions: float | None : Number of times video played to 25%

video_midpoint_completions: float | None : Number of times video played to 50%

video_starts: float | None : Number of video starts

video_third_quartile_completions: float | None : Number of times video played to 75%

video_views: float | None : Number of video views

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.linkedin_ads.models.AirbyteSearchResult[AccountUsersSearchData]
airbyte_agent_sdk.connectors.linkedin_ads.models.AirbyteSearchResult[AccountsSearchData]
airbyte_agent_sdk.connectors.linkedin_ads.models.AirbyteSearchResult[AdCampaignAnalyticsSearchData]
airbyte_agent_sdk.connectors.linkedin_ads.models.AirbyteSearchResult[AdCreativeAnalyticsSearchData]
airbyte_agent_sdk.connectors.linkedin_ads.models.AirbyteSearchResult[CampaignGroupsSearchData]
airbyte_agent_sdk.connectors.linkedin_ads.models.AirbyteSearchResult[CampaignsSearchData]
airbyte_agent_sdk.connectors.linkedin_ads.models.AirbyteSearchResult[ConversionsSearchData]
airbyte_agent_sdk.connectors.linkedin_ads.models.AirbyteSearchResult[CreativesSearchData]

Class variables

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

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

model_config : The type of the None singleton.

AccountUsersSearchResult(**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.linkedin_ads.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

AccountsSearchResult(**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.linkedin_ads.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

AdCampaignAnalyticsSearchResult(**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.linkedin_ads.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

AdCreativeAnalyticsSearchResult(**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.linkedin_ads.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

CampaignGroupsSearchResult(**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.linkedin_ads.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.linkedin_ads.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

ConversionsSearchResult(**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.linkedin_ads.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

CreativesSearchResult(**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.linkedin_ads.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

CampaignGroupsSearchData(**data: Any) : Search result data for campaign_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

account: str | None : Associated account URN

allowed_campaign_types: list[typing.Any] | None : Types of campaigns allowed in this group

backfilled: bool | None : Whether the campaign group is backfilled

id: int | None : Unique campaign group identifier

model_config : The type of the None singleton.

name: str | None : Campaign group name

run_schedule: dict[str, typing.Any] | None : Campaign group run schedule

serving_statuses: list[typing.Any] | None : List of serving statuses

status: str | None : Campaign group status

test: bool | None : Whether this is a test campaign group

total_budget: dict[str, typing.Any] | None : Total budget for the campaign group

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

account: str | None : Associated account URN

associated_entity: str | None : Associated entity URN

audience_expansion_enabled: bool | None : Whether audience expansion is enabled

campaign_group: str | None : Parent campaign group URN

cost_type: str | None : Cost type (CPC CPM etc)

creative_selection: str | None : Creative selection mode

daily_budget: dict[str, typing.Any] | None : Daily budget configuration

format: str | None : Campaign ad format

id: int | None : Unique campaign identifier

locale: dict[str, typing.Any] | None : Campaign locale settings

model_config : The type of the None singleton.

name: str | None : Campaign name

objective_type: str | None : Campaign objective type

offsite_delivery_enabled: bool | None : Whether offsite delivery is enabled

optimization_target_type: str | None : Optimization target type

pacing_strategy: str | None : Budget pacing strategy

run_schedule: dict[str, typing.Any] | None : Campaign run schedule

serving_statuses: list[typing.Any] | None : List of serving statuses

status: str | None : Campaign status

story_delivery_enabled: bool | None : Whether story delivery is enabled

test: bool | None : Whether this is a test campaign

total_budget: dict[str, typing.Any] | None : Total budget configuration

type_: str | None : Campaign type

unit_cost: dict[str, typing.Any] | None : Cost per unit (bid amount)

version: dict[str, typing.Any] | None : Version information

ConversionsSearchData(**data: Any) : Search result data for conversions 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

account: str | None : Associated account URN

associated_campaigns: list[typing.Any] | None : Associated campaigns

attribution_type: str | None : Attribution type for the conversion

campaigns: list[typing.Any] | None : Related campaign URNs

created: int | None : Creation timestamp (epoch milliseconds)

enabled: bool | None : Whether the conversion tracking is enabled

id: int | None : Unique conversion identifier

image_pixel_tag: str | None : Image pixel tracking tag

last_modified: int | None : Last modification timestamp (epoch milliseconds)

model_config : The type of the None singleton.

name: str | None : Conversion name

post_click_attribution_window_size: int | None : Post-click attribution window size in days

type_: str | None : Conversion type

value: dict[str, typing.Any] | None : Conversion value

view_through_attribution_window_size: int | None : View-through attribution window size in days

CreativesSearchData(**data: Any) : Search result data for creatives 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

account: str | None : Associated account URN

campaign: str | None : Parent campaign URN

content: dict[str, typing.Any] | None : Creative content configuration

created_at: int | None : Creation timestamp (epoch milliseconds)

created_by: str | None : URN of the user who created the creative

id: str | None : Unique creative identifier

intended_status: str | None : Intended creative status

is_serving: bool | None : Whether the creative is currently serving

is_test: bool | None : Whether this is a test creative

last_modified_at: int | None : Last modification timestamp (epoch milliseconds)

last_modified_by: str | None : URN of the user who last modified the creative

model_config : The type of the None singleton.

name: str | None : Creative name

serving_hold_reasons: list[typing.Any] | None : Reasons for holding creative from serving

LinkedinAdsAuthConfig(**data: Any) : OAuth 2.0 Authentication

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

client_id: str : OAuth 2.0 application client ID

client_secret: str : OAuth 2.0 application client secret

model_config : The type of the None singleton.

refresh_token: str : OAuth 2.0 refresh token for automatic renewal

LinkedinAdsConnector(auth_config: LinkedinAdsAuthConfig | AirbyteAuthConfig | BaseModel | None = None, on_token_refresh: Any | None = None) : Type-safe Linkedin-Ads API connector.

Auto-generated from OpenAPI specification with full type safety.

Initialize a new linkedin-ads connector instance.

Supports both local and hosted execution modes:

Local mode: Provide connector-specific auth config (e.g., LinkedinAdsAuthConfig)
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 = LinkedinAdsConnector(auth_config=LinkedinAdsAuthConfig(refresh_token="...", client_id="...", client_secret="..."))

Hosted mode with explicit connector_id (no lookup needed)

connector = LinkedinAdsConnector( 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 = LinkedinAdsConnector( 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: "'LinkedinAdsAuthConfig' | None" = None, server_side_oauth_secret_id: str | None = None, name: str | None = None, replication_config: "'LinkedinAdsReplicationConfig' | None" = None, source_template_id: str | None = None) : Create a new hosted connector on Airbyte Cloud.

This factory method:

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

Supports two authentication modes:

Direct credentials: Provide auth_config with typed credentials
Server-side OAuth: Provide server_side_oauth_secret_id from OAuth flow

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. Required unless using server_side_oauth_secret_id. server_side_oauth_secret_id: OAuth secret ID from get_consent_url redirect. When provided, auth_config is not required. 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 LinkedinAdsConnector instance configured in hosted mode

Raises: ValueError: If neither or both auth_config and server_side_oauth_secret_id provided

Example:

Create a new hosted connector with API key auth

connector = await LinkedinAdsConnector.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=LinkedinAdsAuthConfig(refresh_token="...", client_id="...", client_secret="..."), )

With replication config (required for this connector):

With server-side OAuth:

connector = await LinkedinAdsConnector.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", ), server_side_oauth_secret_id="airbyte_oauth_...secret...", replication_config=LinkedinAdsReplicationConfig(start_date="..."), )

Use the connector

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

get_consent_url(*, airbyte_config: AirbyteAuthConfig, redirect_url: str, name: str | None = None, replication_config: "'LinkedinAdsReplicationConfig' | None" = None, source_template_id: str | None = None) : Initiate server-side OAuth flow with auto-source creation.

Returns a consent URL where the end user should be redirected to grant access. After completing consent, the source is automatically created and the user is redirected to your redirect_url with a connector_id query parameter.

Args: airbyte_config: Airbyte hosted auth config with client credentials and workspace_name. Optionally include organization_id for multi-org request routing. redirect_url: URL where users will be redirected after OAuth consent. After consent, user arrives at: redirect_url?connector_id=... name: Optional name for the source. Defaults to connector name + workspace_name. replication_config: Typed replication settings. Merged with OAuth credentials. source_template_id: Source template ID. Required when organization has multiple source templates for this connector type.

Returns: The OAuth consent URL

Example: consent_url = await LinkedinAdsConnector.get_consent_url( airbyte_config=AirbyteAuthConfig( workspace_name="my-workspace", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc", airbyte_client_secret="secret_xyz", ), redirect_url="https://myapp.com/oauth/callback", name="My Linkedin-Ads Source", replication_config=LinkedinAdsReplicationConfig(start_date="..."), )

Redirect user to: consent_url

After consent, user arrives at: https://myapp.com/oauth/callback?connector_id=...

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() @LinkedinAdsConnector.tool_utils async def execute(entity: str, action: str, params: dict): ...

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

@mcp.tool() @LinkedinAdsConnector.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 LinkedinAdsConnector.create(...) print(f"Created connector: {connector.connector_id}")

Methods

check(self) ‑> airbyte_agent_sdk.connectors.linkedin_ads.models.LinkedinAdsCheckResult : 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: LinkedinAdsCheckResult 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', 'get', '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']}")

LinkedinAdsReplicationConfig(**data: Any) : Replication Configuration - Settings for data replication from LinkedIn Ads.

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 : UTC date in the format YYYY-MM-DD. Any data before this date will not be replicated.

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Hosted mode with connector_id (no lookup needed)

Hosted mode with workspace_name (lookup by workspace)

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Create a new hosted connector with API key auth

With replication config (required for this connector):

With server-side OAuth:

Use the connector

Redirect user to: consent_url

After consent, user arrives at: https://myapp.com/oauth/callback?connector_id=...

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