Module airbyte_agent_sdk.connectors.zendesk_talk

Zendesk-Talk connector for Airbyte SDK.

Auto-generated from OpenAPI specification.

Sub-modules

airbyte_agent_sdk.connectors.zendesk_talk.connector
airbyte_agent_sdk.connectors.zendesk_talk.connector_model
airbyte_agent_sdk.connectors.zendesk_talk.models
airbyte_agent_sdk.connectors.zendesk_talk.types

Classes

AccountOverviewSearchData(**data: Any) : Search result data for account_overview 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

average_call_duration: int | None : Average call duration

average_callback_wait_time: int | None : Average callback wait time

average_hold_time: int | None : Average hold time per call

average_queue_wait_time: int | None : Average queue wait time

average_time_to_answer: int | None : Average time to answer

average_wrap_up_time: int | None : Average wrap-up time

current_timestamp: int | None : Current timestamp

max_calls_waiting: int | None : Max calls waiting in queue

max_queue_wait_time: int | None : Max queue wait time

model_config : The type of the None singleton.

total_call_duration: int | None : Total call duration

total_callback_calls: int | None : Total callback calls

total_calls: int | None : Total calls

total_calls_abandoned_in_queue: int | None : Total calls abandoned in queue

total_calls_outside_business_hours: int | None : Total calls outside business hours

total_calls_with_exceeded_queue_wait_time: int | None : Total calls exceeding max queue wait time

total_calls_with_requested_voicemail: int | None : Total calls requesting voicemail

total_embeddable_callback_calls: int | None : Total embeddable callback calls

total_hold_time: int | None : Total hold time

total_inbound_calls: int | None : Total inbound calls

total_outbound_calls: int | None : Total outbound calls

total_textback_requests: int | None : Total textback requests

total_voicemails: int | None : Total voicemails

total_wrap_up_time: int | None : Total wrap-up time

AddressesSearchData(**data: Any) : Search result data for addresses 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

city: str | None : City of the address

country_code: str | None : ISO country code

id: int | None : Unique address identifier

model_config : The type of the None singleton.

name: str | None : Name of the address

provider_reference: str | None : Provider reference of the address

province: str | None : Province of the address

state: str | None : State of the address

street: str | None : Street of the address

zip: str | None : Zip code of the address

AgentsActivitySearchData(**data: Any) : Search result data for agents_activity 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

accepted_third_party_conferences: int | None : Accepted third party conferences

accepted_transfers: int | None : Total transfers accepted

agent_id: int | None : Agent ID

agent_state: str | None : Agent state: online, offline, away, or transfers_only

available_time: int | None : Total time agent was available to answer calls

avatar_url: str | None : URL to agent avatar

average_hold_time: int | None : Average hold time per call

average_talk_time: int | None : Average talk time per call

average_wrap_up_time: int | None : Average wrap-up time per call

away_time: int | None : Total time agent was set to away

call_status: str | None : Agent call status: on_call, wrap_up, or null

calls_accepted: int | None : Total calls accepted

calls_denied: int | None : Total calls denied

calls_missed: int | None : Total calls missed

calls_put_on_hold: int | None : Total calls placed on hold

forwarding_number: str | None : Forwarding number set by the agent

model_config : The type of the None singleton.

name: str | None : Agent name

online_time: int | None : Total online time

started_third_party_conferences: int | None : Started third party conferences

started_transfers: int | None : Total transfers started

total_call_duration: int | None : Total call duration

total_hold_time: int | None : Total hold time across all calls

total_talk_time: int | None : Total talk time (excludes hold)

total_wrap_up_time: int | None : Total wrap-up time

transfers_only_time: int | None : Total time in transfers-only mode

via: str | None : Channel the agent is registered on

AgentsOverviewSearchData(**data: Any) : Search result data for agents_overview 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

average_accepted_transfers: int | None : Average accepted transfers

average_available_time: int | None : Average available time

average_away_time: int | None : Average away time

average_calls_accepted: int | None : Average calls accepted

average_calls_denied: int | None : Average calls denied

average_calls_missed: int | None : Average calls missed

average_calls_put_on_hold: int | None : Average calls put on hold

average_hold_time: int | None : Average hold time

average_online_time: int | None : Average online time

average_started_transfers: int | None : Average started transfers

average_talk_time: int | None : Average talk time

average_transfers_only_time: int | None : Average transfers-only time

average_wrap_up_time: int | None : Average wrap-up time

current_timestamp: int | None : Current timestamp

model_config : The type of the None singleton.

total_accepted_transfers: int | None : Total accepted transfers

total_calls_accepted: int | None : Total calls accepted

total_calls_denied: int | None : Total calls denied

total_calls_missed: int | None : Total calls missed

total_calls_put_on_hold: int | None : Total calls put on hold

total_hold_time: int | None : Total hold time

total_started_transfers: int | None : Total started transfers

total_talk_time: int | None : Total talk time

total_wrap_up_time: int | None : Total wrap-up time

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.zendesk_talk.models.AirbyteSearchResult[AccountOverviewSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[AddressesSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[AgentsActivitySearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[AgentsOverviewSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[CallLegsSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[CallsSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[CurrentQueueActivitySearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[GreetingCategoriesSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[GreetingsSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[IvrsSearchData]
airbyte_agent_sdk.connectors.zendesk_talk.models.AirbyteSearchResult[PhoneNumbersSearchData]

Class variables

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

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

model_config : The type of the None singleton.

AccountOverviewSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

AddressesSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

AgentsActivitySearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

AgentsOverviewSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

CallLegsSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

CallsSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

CurrentQueueActivitySearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

GreetingCategoriesSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

GreetingsSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

IvrsSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

PhoneNumbersSearchResult(**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.zendesk_talk.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

CallLegsSearchData(**data: Any) : Search result data for call_legs 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

agent_id: int | None : Agent ID

available_via: str | None : Channel agent was available through

call_charge: str | None : Call charge amount

call_id: int | None : Associated call ID

completion_status: str | None : Completion status

conference_from: int | None : Conference from time

conference_time: int | None : Conference duration

conference_to: int | None : Conference to time

consultation_from: int | None : Consultation from time

consultation_time: int | None : Consultation duration

consultation_to: int | None : Consultation to time

created_at: str | None : Creation timestamp

duration: int | None : Duration in seconds

forwarded_to: str | None : Number forwarded to

hold_time: int | None : Hold time in seconds

id: int | None : Call leg ID

minutes_billed: int | None : Minutes billed

model_config : The type of the None singleton.

quality_issues: list[typing.Any] | None : Quality issues detected

talk_time: int | None : Talk time in seconds

transferred_from: int | None : Transferred from agent ID

transferred_to: int | None : Transferred to agent ID

type_: str | None : Type of call leg

updated_at: str | None : Last update timestamp

user_id: int | None : User ID

wrap_up_time: int | None : Wrap-up time in seconds

CallsSearchData(**data: Any) : Search result data for calls 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

agent_id: int | None : Agent ID

call_charge: str | None : Call charge amount

call_group_id: int | None : Call group ID

call_recording_consent: str | None : Call recording consent status

call_recording_consent_action: str | None : Recording consent action

call_recording_consent_keypress: str | None : Recording consent keypress

callback: bool | None : Whether this was a callback

callback_source: str | None : Source of the callback

completion_status: str | None : Call completion status

consultation_time: int | None : Consultation time

created_at: str | None : Creation timestamp

customer_requested_voicemail: bool | None : Whether customer requested voicemail

default_group: bool | None : Whether default group was used

direction: str | None : Call direction (inbound/outbound)

duration: int | None : Call duration in seconds

exceeded_queue_time: bool | None : Whether queue time was exceeded

exceeded_queue_wait_time: bool | None : Whether max queue wait time was exceeded

hold_time: int | None : Hold time in seconds

id: int | None : Call ID

ivr_action: str | None : IVR action taken

ivr_destination_group_name: str | None : IVR destination group name

ivr_hops: int | None : Number of IVR hops

ivr_routed_to: str | None : Where IVR routed the call

ivr_time_spent: int | None : Time spent in IVR

minutes_billed: int | None : Minutes billed

model_config : The type of the None singleton.

not_recording_time: int | None : Time not recording

outside_business_hours: bool | None : Whether call was outside business hours

overflowed: bool | None : Whether call overflowed

overflowed_to: str | None : Where call overflowed to

phone_number: str | None : Phone number used

phone_number_id: int | None : Phone number ID

quality_issues: list[typing.Any] | None : Quality issues detected

recording_control_interactions: int | None : Recording control interactions count

recording_time: int | None : Recording time

talk_time: int | None : Talk time in seconds

ticket_id: int | None : Associated ticket ID

time_to_answer: int | None : Time to answer in seconds

updated_at: str | None : Last update timestamp

voicemail: bool | None : Whether it was a voicemail

wait_time: int | None : Wait time in seconds

wrap_up_time: int | None : Wrap-up time in seconds

CurrentQueueActivitySearchData(**data: Any) : Search result data for current_queue_activity 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

agents_online: int | None : Current number of agents online

average_wait_time: int | None : Average wait time for callers in queue (seconds)

callbacks_waiting: int | None : Number of callers in callback queue

calls_waiting: int | None : Number of callers waiting in queue

current_timestamp: int | None : Current timestamp

embeddable_callbacks_waiting: int | None : Number of Web Widget callback requests waiting

longest_wait_time: int | None : Longest wait time for any caller (seconds)

model_config : The type of the None singleton.

GreetingCategoriesSearchData(**data: Any) : Search result data for greeting_categories 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

id: int | None : Greeting category ID

model_config : The type of the None singleton.

name: str | None : Name of the greeting category

GreetingsSearchData(**data: Any) : Search result data for greetings 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

active: bool | None : Whether the greeting is associated with phone numbers

audio_name: str | None : Audio file name

audio_url: str | None : Path to the greeting sound file

category_id: int | None : ID of the greeting category

default: bool | None : Whether this is a system default greeting

default_lang: bool | None : Whether the greeting has a default language

has_sub_settings: bool | None : Sub-settings for categorized greetings

id: str | None : Greeting ID

ivr_ids: list[typing.Any] | None : IDs of IVRs associated with the greeting

model_config : The type of the None singleton.

name: str | None : Name of the greeting

pending: bool | None : Whether the greeting is pending

phone_number_ids: list[typing.Any] | None : IDs of phone numbers associated with the greeting

upload_id: int | None : Upload ID associated with the greeting

IvrsSearchData(**data: Any) : Search result data for ivrs 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

id: int | None : IVR ID

menus: list[typing.Any] | None : List of IVR menus

model_config : The type of the None singleton.

name: str | None : Name of the IVR

phone_number_ids: list[typing.Any] | None : IDs of phone numbers configured with this IVR

phone_number_names: list[typing.Any] | None : Names of phone numbers configured with this IVR

PhoneNumbersSearchData(**data: Any) : Search result data for phone_numbers 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

call_recording_consent: str | None : What call recording consent is set to

capabilities: dict[str, typing.Any] | None : Phone number capabilities (sms, mms, voice)

categorised_greetings: dict[str, typing.Any] | None : Greeting category IDs and names

categorised_greetings_with_sub_settings: dict[str, typing.Any] | None : Greeting categories with associated settings

country_code: str | None : ISO country code for the number

created_at: str | None : Date and time the phone number was created

default_greeting_ids: list[typing.Any] | None : Names of default system greetings

default_group_id: int | None : Default group ID

display_number: str | None : Formatted phone number

external: bool | None : Whether this is an external caller ID number

failover_number: str | None : Failover number associated with the phone number

greeting_ids: list[typing.Any] | None : Custom greeting IDs associated with the phone number

group_ids: list[typing.Any] | None : Array of associated group IDs

id: int | None : Unique phone number identifier

ivr_id: int | None : ID of IVR associated with the phone number

line_type: str | None : Type of line (phone or digital)

location: str | None : Geographical location of the number

model_config : The type of the None singleton.

name: str | None : Nickname if set, otherwise the display number

nickname: str | None : Nickname of the phone number

number: str | None : Phone number digits

outbound_enabled: bool | None : Whether outbound calls are enabled

priority: int | None : Priority level of the phone number

recorded: bool | None : Whether calls are recorded

schedule_id: int | None : ID of schedule associated with the phone number

sms_enabled: bool | None : Whether SMS is enabled

sms_group_id: int | None : Group associated with SMS

token: str | None : Generated token unique for the phone number

toll_free: bool | None : Whether the number is toll-free

transcription: bool | None : Whether voicemail transcription is enabled

voice_enabled: bool | None : Whether voice is enabled

Auto-generated from OpenAPI specification with full type safety.

Initialize a new zendesk-talk connector instance.

Supports both local and hosted execution modes:

Local mode: Provide connector-specific auth config (e.g., ZendeskTalkAuthConfig)
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) subdomain: Your Zendesk subdomain (the part before .zendesk.com in your Zendesk URL) Examples:

Local mode (direct API calls)

connector = ZendeskTalkConnector(auth_config=ZendeskTalkAuthConfig(access_token="...", refresh_token="...", client_id="...", client_secret="..."))

Hosted mode with explicit connector_id (no lookup needed)

connector = ZendeskTalkConnector( 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 = ZendeskTalkConnector( 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: "'ZendeskTalkAuthConfig' | None" = None, server_side_oauth_secret_id: str | None = None, name: str | None = None, replication_config: "'ZendeskTalkReplicationConfig' | 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 ZendeskTalkConnector 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 ZendeskTalkConnector.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=ZendeskTalkAuthConfig(access_token="...", refresh_token="...", client_id="...", client_secret="..."), )

With replication config (required for this connector):

With server-side OAuth:

connector = await ZendeskTalkConnector.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=ZendeskTalkReplicationConfig(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: "'ZendeskTalkReplicationConfig' | 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 ZendeskTalkConnector.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 Zendesk-Talk Source", replication_config=ZendeskTalkReplicationConfig(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() @ZendeskTalkConnector.tool_utils async def execute(entity: str, action: str, params: dict): ...

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

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

Methods

check(self) ‑> airbyte_agent_sdk.connectors.zendesk_talk.models.ZendeskTalkCheckResult : 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: ZendeskTalkCheckResult 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']}")

ZendeskTalkReplicationConfig(**data: Any) : Replication Configuration - Settings for data replication from Zendesk Talk.

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 and time in the format YYYY-MM-DDT00:00:00Z from which to start replicating data.

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Hosted mode with connector_id (no lookup needed)

Hosted mode with workspace_name (lookup by workspace)

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Hosted mode with explicit connector_id (no lookup needed)

Hosted mode with lookup by workspace_name

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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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