Module airbyte_agent_sdk.connectors.salesforce

Salesforce connector for Airbyte SDK.

Auto-generated from OpenAPI specification.

Sub-modules

airbyte_agent_sdk.connectors.salesforce.connector
airbyte_agent_sdk.connectors.salesforce.connector_model
airbyte_agent_sdk.connectors.salesforce.models
airbyte_agent_sdk.connectors.salesforce.types

Classes

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

account_source: str | None : Source of the account record (e.g., Web, Referral)

billing_address: dict[str, typing.Any] | None : Complete billing address as a compound field

billing_city: str | None : City portion of the billing address

billing_country: str | None : Country portion of the billing address

billing_postal_code: str | None : Postal code portion of the billing address

billing_state: str | None : State or province portion of the billing address

billing_street: str | None : Street address portion of the billing address

created_by_id: str | None : ID of the user who created this account

created_date: str | None : Date and time when the account was created

description: str | None : Text description of the account

id: str : Unique identifier for the account record

industry: str | None : Primary business industry of the account

is_deleted: bool | None : Whether the account has been moved to the Recycle Bin

last_activity_date: str | None : Date of the last activity associated with this account

last_modified_by_id: str | None : ID of the user who last modified this account

last_modified_date: str | None : Date and time when the account was last modified

model_config : The type of the None singleton.

name: str | None : Name of the account or company

number_of_employees: int | None : Number of employees at the account

owner_id: str | None : ID of the user who owns this account

parent_id: str | None : ID of the parent account, if this is a subsidiary

phone: str | None : Primary phone number for the account

shipping_address: dict[str, typing.Any] | None : Complete shipping address as a compound field

shipping_city: str | None : City portion of the shipping address

shipping_country: str | None : Country portion of the shipping address

shipping_postal_code: str | None : Postal code portion of the shipping address

shipping_state: str | None : State or province portion of the shipping address

shipping_street: str | None : Street address portion of the shipping address

system_modstamp: str | None : System timestamp when the record was last modified

type_: str | None : Type of account (e.g., Customer, Partner, Competitor)

website: str | None : Website URL for the account

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.salesforce.models.AirbyteSearchResult[AccountsSearchData]
airbyte_agent_sdk.connectors.salesforce.models.AirbyteSearchResult[ContactsSearchData]
airbyte_agent_sdk.connectors.salesforce.models.AirbyteSearchResult[LeadsSearchData]
airbyte_agent_sdk.connectors.salesforce.models.AirbyteSearchResult[OpportunitiesSearchData]
airbyte_agent_sdk.connectors.salesforce.models.AirbyteSearchResult[OpportunityStagesSearchData]
airbyte_agent_sdk.connectors.salesforce.models.AirbyteSearchResult[TasksSearchData]
airbyte_agent_sdk.connectors.salesforce.models.AirbyteSearchResult[UsersSearchData]

Class variables

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

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

model_config : The type of the None singleton.

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.salesforce.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

ContactsSearchResult(**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.salesforce.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

LeadsSearchResult(**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.salesforce.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

OpportunitiesSearchResult(**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.salesforce.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

OpportunityStagesSearchResult(**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.salesforce.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

TasksSearchResult(**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.salesforce.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

UsersSearchResult(**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.salesforce.models.AirbyteSearchResult
pydantic.main.BaseModel
typing.Generic

ContactsSearchData(**data: Any) : Search result data for contacts 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_id: str | None : ID of the account this contact is associated with

created_by_id: str | None : ID of the user who created this contact

created_date: str | None : Date and time when the contact was created

department: str | None : Department within the account where the contact works

email: str | None : Email address of the contact

first_name: str | None : First name of the contact

id: str : Unique identifier for the contact record

is_deleted: bool | None : Whether the contact has been moved to the Recycle Bin

last_activity_date: str | None : Date of the last activity associated with this contact

last_modified_by_id: str | None : ID of the user who last modified this contact

last_modified_date: str | None : Date and time when the contact was last modified

last_name: str | None : Last name of the contact

lead_source: str | None : Source from which this contact originated

mailing_address: dict[str, typing.Any] | None : Complete mailing address as a compound field

mailing_city: str | None : City portion of the mailing address

mailing_country: str | None : Country portion of the mailing address

mailing_postal_code: str | None : Postal code portion of the mailing address

mailing_state: str | None : State or province portion of the mailing address

mailing_street: str | None : Street address portion of the mailing address

mobile_phone: str | None : Mobile phone number of the contact

model_config : The type of the None singleton.

name: str | None : Full name of the contact (read-only, concatenation of first and last name)

owner_id: str | None : ID of the user who owns this contact

phone: str | None : Business phone number of the contact

reports_to_id: str | None : ID of the contact this contact reports to

system_modstamp: str | None : System timestamp when the record was last modified

title: str | None : Job title of the contact

LeadsSearchData(**data: Any) : Search result data for leads entity.

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

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

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

Ancestors (in MRO)

pydantic.main.BaseModel

Class variables

address: dict[str, typing.Any] | None : Complete address as a compound field

city: str | None : City portion of the address

company: str | None : Company or organization the lead works for

converted_account_id: str | None : ID of the account created when lead was converted

converted_contact_id: str | None : ID of the contact created when lead was converted

converted_date: str | None : Date when the lead was converted

converted_opportunity_id: str | None : ID of the opportunity created when lead was converted

country: str | None : Country portion of the address

created_by_id: str | None : ID of the user who created this lead

created_date: str | None : Date and time when the lead was created

email: str | None : Email address of the lead

first_name: str | None : First name of the lead

id: str : Unique identifier for the lead record

industry: str | None : Industry the lead's company operates in

is_converted: bool | None : Whether the lead has been converted to an account, contact, and opportunity

is_deleted: bool | None : Whether the lead has been moved to the Recycle Bin

last_activity_date: str | None : Date of the last activity associated with this lead

last_modified_by_id: str | None : ID of the user who last modified this lead

last_modified_date: str | None : Date and time when the lead was last modified

last_name: str | None : Last name of the lead

lead_source: str | None : Source from which this lead originated

mobile_phone: str | None : Mobile phone number of the lead

model_config : The type of the None singleton.

name: str | None : Full name of the lead (read-only, concatenation of first and last name)

number_of_employees: int | None : Number of employees at the lead's company

owner_id: str | None : ID of the user who owns this lead

phone: str | None : Phone number of the lead

postal_code: str | None : Postal code portion of the address

rating: str | None : Rating of the lead (e.g., Hot, Warm, Cold)

state: str | None : State or province portion of the address

status: str | None : Current status of the lead in the sales process

street: str | None : Street address portion of the address

system_modstamp: str | None : System timestamp when the record was last modified

title: str | None : Job title of the lead

website: str | None : Website URL for the lead's company

OpportunitiesSearchData(**data: Any) : Search result data for opportunities 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_id: str | None : ID of the account associated with this opportunity

amount: float | None : Estimated total sale amount

campaign_id: str | None : ID of the campaign that generated this opportunity

close_date: str | None : Expected close date for the opportunity

contact_id: str | None : ID of the primary contact for this opportunity

created_by_id: str | None : ID of the user who created this opportunity

created_date: str | None : Date and time when the opportunity was created

description: str | None : Text description of the opportunity

expected_revenue: float | None : Expected revenue based on amount and probability

forecast_category: str | None : Forecast category for this opportunity

forecast_category_name: str | None : Name of the forecast category

id: str : Unique identifier for the opportunity record

is_closed: bool | None : Whether the opportunity is closed

is_deleted: bool | None : Whether the opportunity has been moved to the Recycle Bin

is_won: bool | None : Whether the opportunity was won

last_activity_date: str | None : Date of the last activity associated with this opportunity

last_modified_by_id: str | None : ID of the user who last modified this opportunity

last_modified_date: str | None : Date and time when the opportunity was last modified

lead_source: str | None : Source from which this opportunity originated

model_config : The type of the None singleton.

name: str | None : Name of the opportunity

next_step: str | None : Description of the next step in closing the opportunity

owner_id: str | None : ID of the user who owns this opportunity

probability: float | None : Likelihood of closing the opportunity (percentage)

stage_name: str | None : Current stage of the opportunity in the sales process

system_modstamp: str | None : System timestamp when the record was last modified

type_: str | None : Type of opportunity (e.g., New Business, Existing Business)

OpportunityStagesSearchData(**data: Any) : Search result data for opportunity_stages 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

api_name: str | None : API name of the stage used in code and integrations

created_by_id: str | None : ID of the user who created this stage

created_date: str | None : Date and time when the stage was created

default_probability: float | None : Default probability percentage for opportunities at this stage

description: str | None : Description of the stage

forecast_category: str | None : Forecast category for opportunities at this stage

forecast_category_name: str | None : Display name of the forecast category

id: str : Unique identifier for the opportunity stage record

is_active: bool | None : Whether the stage is currently active and can be used

is_closed: bool | None : Whether opportunities at this stage are considered closed

is_won: bool | None : Whether opportunities at this stage are considered won

last_modified_by_id: str | None : ID of the user who last modified this stage

last_modified_date: str | None : Date and time when the stage was last modified

master_label: str | None : Display label for the stage

model_config : The type of the None singleton.

sort_order: int | None : Order in which the stage appears in the sales process

system_modstamp: str | None : System timestamp when the record was last modified

SalesforceAuthConfig(**data: Any) : Salesforce OAuth 2.0

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 | None : Connected App Consumer Key

client_secret: str | None : Connected App Consumer Secret

model_config : The type of the None singleton.

refresh_token: str : OAuth refresh token for automatic token renewal

Auto-generated from OpenAPI specification with full type safety.

Initialize a new salesforce connector instance.

Supports both local and hosted execution modes:

Local mode: Provide connector-specific auth config (e.g., SalesforceAuthConfig)
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) instance_url: Your Salesforce instance URL (e.g., https://na1.salesforce.com) Examples:

Local mode (direct API calls)

connector = SalesforceConnector(auth_config=SalesforceAuthConfig(refresh_token="...", client_id="...", client_secret="..."))

Hosted mode with explicit connector_id (no lookup needed)

connector = SalesforceConnector( 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 = SalesforceConnector( 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: "'SalesforceAuthConfig' | None" = None, server_side_oauth_secret_id: str | None = None, name: str | None = None, replication_config: dict[str, Any] | 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: 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 SalesforceConnector 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 SalesforceConnector.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=SalesforceAuthConfig(refresh_token="...", client_id="...", client_secret="..."), )

With server-side OAuth:

connector = await SalesforceConnector.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...", )

Use the connector

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

get_consent_url(*, airbyte_config: AirbyteAuthConfig, redirect_url: str, name: str | None = None, replication_config: dict[str, Any] | None = None, source_template_id: str | None = None) ‑> str : 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: Optional replication settings dict. 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 SalesforceConnector.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 Salesforce Source", )

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

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

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

Methods

check(self) ‑> airbyte_agent_sdk.connectors.salesforce.models.SalesforceCheckResult : 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: SalesforceCheckResult 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', 'api_search', 'download', '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']}")

TasksSearchData(**data: Any) : Search result data for tasks 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_id: str | None : ID of the account associated with this task

activity_date: str | None : Due date for the task

call_disposition: str | None : Result of the call, if this task represents a call

call_duration_in_seconds: int | None : Duration of the call in seconds

call_type: str | None : Type of call (Inbound, Outbound, Internal)

completed_date_time: str | None : Date and time when the task was completed

created_by_id: str | None : ID of the user who created this task

created_date: str | None : Date and time when the task was created

description: str | None : Text description or notes about the task

id: str : Unique identifier for the task record

is_closed: bool | None : Whether the task has been completed

is_deleted: bool | None : Whether the task has been moved to the Recycle Bin

is_high_priority: bool | None : Whether the task is marked as high priority

last_modified_by_id: str | None : ID of the user who last modified this task

last_modified_date: str | None : Date and time when the task was last modified

model_config : The type of the None singleton.

owner_id: str | None : ID of the user who owns this task

priority: str | None : Priority level of the task (High, Normal, Low)

status: str | None : Current status of the task

subject: str | None : Subject or title of the task

system_modstamp: str | None : System timestamp when the record was last modified

task_subtype: str | None : Subtype of the task (e.g., Call, Email, Task)

type_: str | None : Type of task

what_id: str | None : ID of the related object (Account, Opportunity, etc.)

who_id: str | None : ID of the related person (Contact or Lead)

UsersSearchData(**data: Any) : Search result data for 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_id: str | None : ID of the account associated with this user (for portal users)

alias: str | None : Short name used to identify the user in list views and reports

city: str | None : City portion of the user's address

company_name: str | None : Name of the user's company

contact_id: str | None : ID of the contact associated with this user (for portal users)

country: str | None : Country portion of the user's address

created_by_id: str | None : ID of the user who created this user record

created_date: str | None : Date and time when the user was created

department: str | None : Department within the organization

division: str | None : Division within the organization

email: str | None : Email address of the user

employee_number: str | None : Employee number or ID assigned by the organization

first_name: str | None : First name of the user

id: str : Unique identifier for the user record

is_active: bool | None : Whether the user is active and can log in

last_login_date: str | None : Date and time of the user's most recent login

last_modified_by_id: str | None : ID of the user who last modified this user record

last_modified_date: str | None : Date and time when the user was last modified

last_name: str | None : Last name of the user

manager_id: str | None : ID of the user's manager

mobile_phone: str | None : Mobile phone number of the user

model_config : The type of the None singleton.

name: str | None : Full name of the user

phone: str | None : Business phone number of the user

postal_code: str | None : Postal code portion of the user's address

profile_id: str | None : ID of the user's profile

state: str | None : State or province portion of the user's address

street: str | None : Street address of the user

system_modstamp: str | None : System timestamp when the record was last modified

title: str | None : Job title of the user

user_role_id: str | None : ID of the user's role in the organization

user_type: str | None : Type of user license (e.g., Standard, PowerPartner)

username: str | None : Username for logging into Salesforce (unique across all orgs)

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Hosted mode with workspace_name (lookup by workspace)

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Local mode (direct API calls)

Hosted mode with explicit connector_id (no lookup needed)

Hosted mode with lookup by workspace_name

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Static methods​

Create a new hosted connector with API key auth

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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