Zendesk-Support authentication

This connector is optimized for AI agents. For the data replication connector, see Zendesk Support.

This page documents the authentication and configuration options for the Zendesk-Support agent connector.

Hosted mode (most cases)

In hosted mode, create the connector through the Airbyte Agent CLI or API, then execute operations using the CLI, Python SDK, or API. If you need a step-by-step guide, see the developer quickstart.

OAuth

Use the CLI for hosted OAuth connector creation when possible. It opens the hosted setup flow and avoids passing connector secrets through the command line:

airbyte-agent login
airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-support"
}'

For API-first use cases, create a connector with OAuth credentials directly.

credentials fields you need:

Field Name	Type	Required	Description
`access_token`	`str`	Yes	OAuth 2.0 access token
`refresh_token`	`str`	No	OAuth 2.0 refresh token (optional)

Example request:

curl -X POST "https://api.airbyte.ai/api/v1/integrations/connectors" \
  -H "Authorization: Bearer <YOUR_BEARER_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_name": "<WORKSPACE_NAME>",
    "connector_type": "Zendesk-Support",
    "name": "My Zendesk-Support Connector",
    "credentials": {
      "access_token": "<OAuth 2.0 access token>",
      "refresh_token": "<OAuth 2.0 refresh token (optional)>"
    }
  }'

Token

Create a connector with Token credentials.

credentials fields you need:

Field Name	Type	Required	Description
`email`	`str`	Yes	Your Zendesk account email address
`api_token`	`str`	Yes	Your Zendesk API token from Admin Center

Example request:

curl -X POST "https://api.airbyte.ai/api/v1/integrations/connectors" \
  -H "Authorization: Bearer <YOUR_BEARER_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_name": "<WORKSPACE_NAME>",
    "connector_type": "Zendesk-Support",
    "name": "My Zendesk-Support Connector",
    "credentials": {
      "email": "<Your Zendesk account email address>",
      "api_token": "<Your Zendesk API token from Admin Center>"
    }
  }'

Execution

After creating the connector, execute operations using the CLI, Python SDK, or API. If your Airbyte client can access multiple organizations, set the default organization with airbyte-agent organizations use, include organization_id in AirbyteAuthConfig, or include X-Organization-Id in raw API calls.

CLI

Authenticate with Airbyte:

airbyte-agent login

Create the connector. The CLI opens the hosted setup flow:

airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-support"
}'

Describe the connector to see its supported entities and actions:

airbyte-agent connectors describe --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-support"
}'

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-support",
  "entity": "<entity>",
  "action": "<action>",
  "params": {}
}'

Python SDK

The connect() factory returns a fully typed ZendeskSupportConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

@tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ZendeskSupportConnector.tool_utils(framework="openai_agents")
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Zendesk-Support Assistant", tools=[zendesk_support_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

mcp = FastMCP("Zendesk-Support Agent")

@mcp.tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment): Pydantic AI

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

@tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ZendeskSupportConnector.tool_utils(framework="openai_agents")
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Zendesk-Support Assistant", tools=[zendesk_support_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

mcp = FastMCP("Zendesk-Support Agent")

@mcp.tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

API

curl -X POST 'https://api.airbyte.ai/api/v1/integrations/connectors/<connector_id>/execute' \
  -H 'Authorization: Bearer <YOUR_BEARER_TOKEN>' \
  -H 'X-Organization-Id: <YOUR_ORGANIZATION_ID>' \
  -H 'Content-Type: application/json' \
  -d '{"entity": "<entity>", "action": "<action>", "params": {}}'

Open source mode

In open source mode, provide API credentials directly to the connector.

OAuth

credentials fields you need:

Field Name	Type	Required	Description
`access_token`	`str`	Yes	OAuth 2.0 access token
`refresh_token`	`str`	No	OAuth 2.0 refresh token (optional)

Example request:

from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.connectors.zendesk_support.models import ZendeskSupportOauth20AuthConfig

connector = ZendeskSupportConnector(
    auth_config=ZendeskSupportOauth20AuthConfig(
        access_token="<OAuth 2.0 access token>",
        refresh_token="<OAuth 2.0 refresh token (optional)>"
    )
)

Token

credentials fields you need:

Field Name	Type	Required	Description
`email`	`str`	Yes	Your Zendesk account email address
`api_token`	`str`	Yes	Your Zendesk API token from Admin Center

Example request:

from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.connectors.zendesk_support.models import ZendeskSupportApiTokenAuthConfig

connector = ZendeskSupportConnector(
    auth_config=ZendeskSupportApiTokenAuthConfig(
        email="<Your Zendesk account email address>",
        api_token="<Your Zendesk API token from Admin Center>"
    )
)

Configuration

The Zendesk-Support connector also needs these configuration values to construct the base API URL.

Hosted CLI: airbyte-agent connectors create doesn't currently accept these configuration fields directly. For hosted connectors that need these values, create the connector with the hosted API replication_config, then use the CLI for describe and execute operations after creation.
Hosted API: pass these values in the connector creation replication_config.
Open source mode: provide these values with your local connector setup so the connector can build the correct API base URL.

Variable	Type	Required	Default	Description
`subdomain`	`string`	Yes	your-subdomain	Your Zendesk subdomain

Hosted mode (most cases)​

OAuth​

Token​

Execution​

Open source mode​

OAuth​

Token​

Configuration​

Hosted mode (most cases)

OAuth

Token

Execution

Open source mode

OAuth

Token

Configuration