Zendesk-Talk

The Zendesk-Talk agent connector is a Python package that equips AI agents to interact with Zendesk-Talk through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.

Connector for the Zendesk Talk (Voice) API. Provides access to phone numbers, addresses, greetings, IVR configurations, call data, and agent/account statistics for Zendesk Talk voice support channels.

Example questions

The Zendesk-Talk connector is optimized to handle prompts like these.

List all phone numbers in our Zendesk Talk account
Show all addresses on file
List all IVR configurations
Show all greetings
List greeting categories
Show agent activity statistics
Show the account overview stats
Show current queue activity
Which phone numbers have SMS enabled?
Find agents who have missed the most calls today
What is the average call duration across all calls?
Which phone numbers are toll-free?

Unsupported questions

The Zendesk-Talk connector isn't currently able to handle prompts like these.

Create a new phone number
Delete an IVR configuration
Update a greeting
Make an outbound call

Installation

uv pip install airbyte-agent-zendesk-talk

Usage

Connectors can run in open source or hosted mode.

Open source

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

from airbyte_agent_zendesk_talk import ZendeskTalkConnector
from airbyte_agent_zendesk_talk.models import ZendeskTalkApiTokenAuthConfig

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

@agent.tool_plain # assumes you're using Pydantic AI
@ZendeskTalkConnector.tool_utils
async def zendesk_talk_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Hosted

In hosted mode, API credentials are stored securely in Airbyte Cloud. You provide your Airbyte credentials instead. If your Airbyte client can access multiple organizations, also set organization_id.

This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.

from airbyte_agent_zendesk_talk import ZendeskTalkConnector, AirbyteAuthConfig

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

@agent.tool_plain # assumes you're using Pydantic AI
@ZendeskTalkConnector.tool_utils
async def zendesk_talk_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Full documentation

Entities and actions

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

Entity	Actions
Phone Numbers	List, Get, Search
Addresses	List, Get, Search
Greetings	List, Get, Search
Greeting Categories	List, Get, Search
Ivrs	List, Get, Search
Agents Activity	List, Search
Agents Overview	List, Search
Account Overview	List, Search
Current Queue Activity	List, Search
Calls	List, Search
Call Legs	List, Search

Authentication

For all authentication options, see the connector's authentication documentation.

Zendesk-Talk API docs

See the official Zendesk-Talk API reference.

Version information

Package version: 0.1.0
Connector version: 1.0.0
Generated with Connector SDK commit SHA: 8908ac87ea5892c41d386207a3f2cd5176e49825
Changelog: View changelog

Example questions​

Unsupported questions​

Installation​

Usage​

Open source​

Hosted​

Full documentation​

Entities and actions​

Authentication​

Zendesk-Talk API docs​

Version information​