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Zendesk-Support

The Zendesk-Support agent connector is a Python package that equips AI agents to interact with Zendesk-Support 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.

Zendesk Support is a customer service platform that helps businesses manage support tickets, customer interactions, and help center content. This connector provides access to tickets, users, organizations, groups, comments, attachments, automations, triggers, macros, views, satisfaction ratings, SLA policies, and help center articles for customer support analytics and service performance insights.

Example prompts​

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

  • Show me the tickets assigned to me last week
  • List all unresolved tickets
  • Show me the details of recent tickets
  • Create a new ticket with subject 'Login issue' and priority high
  • Update ticket 12345 to status solved
  • Add a comment to ticket 12345 saying 'This has been resolved'
  • Set the priority of ticket 12345 to urgent and assign it to agent 98765
  • Create a new end-user named 'Jane Doe' with email jane@example.com
  • Update user 54321 with notes 'VIP customer'
  • What are the top 5 support issues our organization has faced this month?
  • Analyze the satisfaction ratings for our support team in the last 30 days
  • Compare ticket resolution times across different support groups
  • Identify the most common ticket fields used in our support workflow
  • Summarize the performance of our SLA policies this quarter

Unsupported prompts​

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

  • Delete these old support tickets
  • Merge two tickets together
  • Export all tickets to a CSV file

Entities and actions​

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

EntityActions
TicketsList, Create, Get, Update, Context Store Search, Context Store SQL Query
Ticket CommentsCreate, List, Context Store Search, Context Store SQL Query, Semantic Search
Ticket Bulk UpdatesCreate
Deleted TicketsList, Context Store Search, Context Store SQL Query
UsersList, Create, Get, Update, Context Store Search, Context Store SQL Query
OrganizationsList, Get, Context Store Search, Context Store SQL Query
GroupsList, Get, Context Store Search, Context Store SQL Query
AttachmentsGet, Download
Ticket AuditsList, List, Context Store Search, Context Store SQL Query
Ticket MetricsList, Context Store Search, Context Store SQL Query
Ticket FieldsList, Get, Context Store Search, Context Store SQL Query
BrandsList, Get, Context Store Search, Context Store SQL Query
ViewsList, Get
MacrosGet, List, Context Store Search, Context Store SQL Query
TriggersList, Get, Context Store Search, Context Store SQL Query
AutomationsList, Get, Context Store Search, Context Store SQL Query
TagsList, Context Store Search, Context Store SQL Query
Satisfaction RatingsList, Get, Context Store Search, Context Store SQL Query
Group MembershipsList, Context Store Search, Context Store SQL Query
Organization MembershipsList, Context Store Search, Context Store SQL Query
Sla PoliciesList, Get, Context Store Search, Context Store SQL Query
Ticket FormsList, Get, Context Store Search, Context Store SQL Query
ArticlesList, Get, Context Store Search, Context Store SQL Query
Article AttachmentsList, Get, Download, Context Store Search, Context Store SQL Query

Zendesk-Support API docs​

See the official Zendesk-Support API reference.

Interfaces​

Use the Zendesk-Support connector through the Airbyte Agent CLI, the Python SDK, or the API.

CLI​

Install the CLI:

curl -fsSL https://airbyte.ai/install.sh | bash

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": "tickets",
  "action": "list"
}'

Python SDK​

Installation​

uv pip install airbyte-agent-sdk

Usage​

Connectors can run in hosted or open source mode.

Hosted​

In hosted mode, API credentials are stored securely in Airbyte Agents. 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.

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

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
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>")

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use ZendeskSupportConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

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.agent_tool(
    framework="pydantic_ai",
    inspect_tool="zendesk_support_inspect",
    docs_tool="zendesk_support_read_docs",
)
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@ZendeskSupportConnector.agent_tool(framework="pydantic_ai")
async def zendesk_support_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@ZendeskSupportConnector.agent_tool(framework="pydantic_ai")
async def zendesk_support_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

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

@ZendeskSupportConnector.agent_tool(
    inspect_tool="zendesk_support_inspect",
    docs_tool="zendesk_support_read_docs",
)
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ZendeskSupportConnector.agent_tool()
async def zendesk_support_inspect():
    return await connector.inspect_connector()

@ZendeskSupportConnector.agent_tool()
async def zendesk_support_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (zendesk_support_inspect, zendesk_support_read_docs, zendesk_support_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated ZendeskSupportConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or ZendeskSupportConnector.agent_tool above.

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 {})

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

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
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>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Open source​

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

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
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>"
    ),
    subdomain="<Your Zendesk subdomain>"
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use ZendeskSupportConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

Pydantic AI
from pydantic_ai import Agent
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>"
    ),
    subdomain="<Your Zendesk subdomain>"
)

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

@agent.tool_plain
@ZendeskSupportConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="zendesk_support_inspect",
    docs_tool="zendesk_support_read_docs",
)
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@ZendeskSupportConnector.agent_tool(framework="pydantic_ai")
async def zendesk_support_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@ZendeskSupportConnector.agent_tool(framework="pydantic_ai")
async def zendesk_support_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
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>"
    ),
    subdomain="<Your Zendesk subdomain>"
)

@ZendeskSupportConnector.agent_tool(
    inspect_tool="zendesk_support_inspect",
    docs_tool="zendesk_support_read_docs",
)
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ZendeskSupportConnector.agent_tool()
async def zendesk_support_inspect():
    return await connector.inspect_connector()

@ZendeskSupportConnector.agent_tool()
async def zendesk_support_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (zendesk_support_inspect, zendesk_support_read_docs, zendesk_support_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated ZendeskSupportConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or ZendeskSupportConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
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>"
    ),
    subdomain="<Your Zendesk subdomain>"
)

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 {})

Authentication​

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

IP allow list​

If your organization restricts access to specific IPs, add the Airbyte Agents IP addresses to your allow list.

Version information​

Connector version: 0.1.20

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