Module airbyte_agent_sdk.connectors.confluence

Confluence connector for Airbyte SDK.

Auto-generated from OpenAPI specification.

Sub-modules

airbyte_agent_sdk.connectors.confluence.connector
airbyte_agent_sdk.connectors.confluence.connector_model
airbyte_agent_sdk.connectors.confluence.models
airbyte_agent_sdk.connectors.confluence.types

Classes

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.confluence.models.AirbyteSearchResult[AuditSearchData]
airbyte_agent_sdk.connectors.confluence.models.AirbyteSearchResult[BlogPostsSearchData]
airbyte_agent_sdk.connectors.confluence.models.AirbyteSearchResult[GroupsSearchData]
airbyte_agent_sdk.connectors.confluence.models.AirbyteSearchResult[PagesSearchData]
airbyte_agent_sdk.connectors.confluence.models.AirbyteSearchResult[SpacesSearchData]

Class variables

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

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

model_config : The type of the None singleton.

AuditSearchResult(**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.confluence.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

BlogPostsSearchResult(**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.confluence.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

GroupsSearchResult(**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.confluence.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

PagesSearchResult(**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.confluence.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

SpacesSearchResult(**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.confluence.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

AuditSearchData(**data: Any) : Search result data for audit 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

affected_object: dict[str, typing.Any] | None : The object that was affected by the audit event.

associated_objects: list[typing.Any] | None : Any associated objects related to the audit event.

author: dict[str, typing.Any] | None : The user who triggered the audit event.

category: str | None : The category under which the audit event falls.

changed_values: list[typing.Any] | None : Details of the values that were changed during the audit event.

creation_date: int | None : The date and time when the audit event was created.

description: str | None : A detailed description of the audit event.

model_config : The type of the None singleton.

remote_address: str | None : The IP address from which the audit event originated.

summary: str | None : A brief summary or title describing the audit event.

super_admin: bool | None : Indicates if the user triggering the audit event is a super admin.

sys_admin: bool | None : Indicates if the user triggering the audit event is a system admin.

BlogPostsSearchData(**data: Any) : Search result data for blog_posts 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

author_id: str | None : ID of the user who created the blog post

body: dict[str, typing.Any] | None : Blog post body content

created_at: str | None : Timestamp when the blog post was created

id: str | None : Unique blog post identifier

links: dict[str, typing.Any] | None : Links related to the blog post

model_config : The type of the None singleton.

space_id: str | None : ID of the space containing this blog post

status: str | None : Blog post status (current, draft, trashed)

title: str | None : Blog post title

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

ConfluenceAuthConfig(**data: Any) : Confluence API Token Authentication - Authenticate using your Atlassian account email and API 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

model_config : The type of the None singleton.

password: str : Your Confluence API token from https://id.atlassian.com/manage-profile/security/api-tokens

username: str : Your Atlassian account email address

Auto-generated from OpenAPI specification with full type safety.

Initialize a new confluence connector instance.

Supports both local and hosted execution modes:

Local mode: Provide connector-specific auth config (e.g., ConfluenceAuthConfig)
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 Confluence Cloud subdomain (e.g., mycompany for mycompany.atlassian.net) Examples:

Local mode (direct API calls)

connector = ConfluenceConnector(auth_config=ConfluenceAuthConfig(username="...", password="..."))

Hosted mode with explicit connector_id (no lookup needed)

connector = ConfluenceConnector( 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 = ConfluenceConnector( 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: "'ConfluenceAuthConfig'", name: str | None = None, replication_config: dict[str, Any] | None = None, source_template_id: str | None = None) ‑> airbyte_agent_sdk.connectors.confluence.connector.ConfluenceConnector : 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

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: Optional replication settings dict. 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 ConfluenceConnector instance configured in hosted mode

Example:

Create a new hosted connector with API key auth

connector = await ConfluenceConnector.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=ConfluenceAuthConfig(username="...", password="..."), )

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

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

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

Methods

check(self) ‑> airbyte_agent_sdk.connectors.confluence.models.ConfluenceCheckResult : 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: ConfluenceCheckResult 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']}")

GroupsSearchData(**data: Any) : Search result data for 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

id: str | None : The unique identifier of the group

links: dict[str, typing.Any] | None : Links related to the group

model_config : The type of the None singleton.

name: str | None : The name of the group

type_: str | None : The type of group

PagesSearchData(**data: Any) : Search result data for pages 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

author_id: str | None : ID of the user who created the page

body: dict[str, typing.Any] | None : Page body content

created_at: str | None : Timestamp when the page was created

id: str | None : Unique page identifier

last_owner_id: str | None : ID of the previous page owner

links: dict[str, typing.Any] | None : Links related to the page

model_config : The type of the None singleton.

owner_id: str | None : ID of the current page owner

parent_id: str | None : ID of the parent page

parent_type: str | None : Type of the parent (page or space)

position: int | None : Position of the page among siblings

space_id: str | None : ID of the space containing this page

status: str | None : Page status (current, archived, trashed, draft)

title: str | None : Page title

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

SpacesSearchData(**data: Any) : Search result data for spaces 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

author_id: str | None : ID of the user who created the space

created_at: str | None : Timestamp when the space was created

description: dict[str, typing.Any] | None : Space description in various formats

homepage_id: str | None : ID of the space homepage

icon: dict[str, typing.Any] | None : Space icon information

id: str | None : Unique space identifier

key: str | None : Space key

links: dict[str, typing.Any] | None : Links related to the space

model_config : The type of the None singleton.

name: str | None : Space name

status: str | None : Space status (current or archived)

type_: str | None : Space type (global or personal)

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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

Use the connector

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