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Snapchat-Marketing

The Snapchat-Marketing agent connector is a Python package that equips AI agents to interact with Snapchat-Marketing 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 Snapchat Marketing API (Ads API). Provides access to Snapchat advertising entities including organizations, ad accounts, campaigns, ad squads, ads, creatives, media, and audience segments. Supports OAuth2 authentication with automatic token refresh.

Example prompts​

The Snapchat-Marketing connector is optimized to handle prompts like these.

  • List all organizations I belong to
  • Show me all ad accounts for my organization
  • List all campaigns in my ad account
  • Show me the ad squads for my ad account
  • List all ads in my ad account
  • Show me the creatives for my ad account
  • List all media files in my ad account
  • Show me the audience segments in my ad account
  • Which campaigns are currently active?
  • What ad squads have the highest daily budget?
  • Show me ads that are pending review
  • Find campaigns created in the last month

Unsupported prompts​

The Snapchat-Marketing connector isn't currently able to handle prompts like these.

  • Create a new campaign
  • Update an ad's status
  • Delete a creative
  • Show me ad performance statistics

Entities and actions​

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

EntityActions
OrganizationsList, Get, Context Store Search, Context Store SQL Query
AdaccountsList, Get, Context Store Search, Context Store SQL Query
CampaignsList, Get, Context Store Search, Context Store SQL Query
AdsquadsList, Get, Context Store Search, Context Store SQL Query
AdsList, Get, Context Store Search, Context Store SQL Query
CreativesList, Get, Context Store Search, Context Store SQL Query
MediaList, Get, Context Store Search, Context Store SQL Query
SegmentsList, Get, Context Store Search, Context Store SQL Query

Snapchat-Marketing API docs​

See the official Snapchat-Marketing API reference.

Interfaces​

Use the Snapchat-Marketing 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": "snapchat-marketing"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "snapchat-marketing",
  "entity": "organizations",
  "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 SnapchatMarketingConnector 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.snapchat_marketing import SnapchatMarketingConnector

connector = connect("snapchat-marketing", 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 SnapchatMarketingConnector.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.snapchat_marketing import SnapchatMarketingConnector

connector = connect("snapchat-marketing", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@SnapchatMarketingConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="snapchat_marketing_inspect",
    docs_tool="snapchat_marketing_read_docs",
)
async def snapchat_marketing_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@SnapchatMarketingConnector.agent_tool(framework="pydantic_ai")
async def snapchat_marketing_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.snapchat_marketing import SnapchatMarketingConnector

connector = connect("snapchat-marketing", workspace_name="<your_workspace_name>")

@SnapchatMarketingConnector.agent_tool(
    inspect_tool="snapchat_marketing_inspect",
    docs_tool="snapchat_marketing_read_docs",
)
async def snapchat_marketing_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@SnapchatMarketingConnector.agent_tool()
async def snapchat_marketing_inspect():
    return await connector.inspect_connector()

@SnapchatMarketingConnector.agent_tool()
async def snapchat_marketing_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 (snapchat_marketing_inspect, snapchat_marketing_read_docs, snapchat_marketing_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 SnapchatMarketingConnector.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 SnapchatMarketingConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.snapchat_marketing import SnapchatMarketingConnector

connector = connect("snapchat-marketing", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@SnapchatMarketingConnector.tool_utils
async def snapchat_marketing_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.snapchat_marketing import SnapchatMarketingConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = SnapchatMarketingConnector(
    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.snapchat_marketing import SnapchatMarketingConnector
from airbyte_agent_sdk.connectors.snapchat_marketing.models import SnapchatMarketingAuthConfig

connector = SnapchatMarketingConnector(
    auth_config=SnapchatMarketingAuthConfig(
        client_id="<The Client ID of your Snapchat developer application>",
        client_secret="<The Client Secret of your Snapchat developer application>",
        refresh_token="<Refresh Token to renew the expired Access Token>"
    )
)

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 SnapchatMarketingConnector.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.snapchat_marketing import SnapchatMarketingConnector
from airbyte_agent_sdk.connectors.snapchat_marketing.models import SnapchatMarketingAuthConfig

connector = SnapchatMarketingConnector(
    auth_config=SnapchatMarketingAuthConfig(
        client_id="<The Client ID of your Snapchat developer application>",
        client_secret="<The Client Secret of your Snapchat developer application>",
        refresh_token="<Refresh Token to renew the expired Access Token>"
    )
)

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

@agent.tool_plain
@SnapchatMarketingConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="snapchat_marketing_inspect",
    docs_tool="snapchat_marketing_read_docs",
)
async def snapchat_marketing_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@SnapchatMarketingConnector.agent_tool(framework="pydantic_ai")
async def snapchat_marketing_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.snapchat_marketing import SnapchatMarketingConnector
from airbyte_agent_sdk.connectors.snapchat_marketing.models import SnapchatMarketingAuthConfig

connector = SnapchatMarketingConnector(
    auth_config=SnapchatMarketingAuthConfig(
        client_id="<The Client ID of your Snapchat developer application>",
        client_secret="<The Client Secret of your Snapchat developer application>",
        refresh_token="<Refresh Token to renew the expired Access Token>"
    )
)

@SnapchatMarketingConnector.agent_tool(
    inspect_tool="snapchat_marketing_inspect",
    docs_tool="snapchat_marketing_read_docs",
)
async def snapchat_marketing_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@SnapchatMarketingConnector.agent_tool()
async def snapchat_marketing_inspect():
    return await connector.inspect_connector()

@SnapchatMarketingConnector.agent_tool()
async def snapchat_marketing_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 (snapchat_marketing_inspect, snapchat_marketing_read_docs, snapchat_marketing_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 SnapchatMarketingConnector.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 SnapchatMarketingConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.snapchat_marketing import SnapchatMarketingConnector
from airbyte_agent_sdk.connectors.snapchat_marketing.models import SnapchatMarketingAuthConfig

connector = SnapchatMarketingConnector(
    auth_config=SnapchatMarketingAuthConfig(
        client_id="<The Client ID of your Snapchat developer application>",
        client_secret="<The Client Secret of your Snapchat developer application>",
        refresh_token="<Refresh Token to renew the expired Access Token>"
    )
)

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

@agent.tool_plain
@SnapchatMarketingConnector.tool_utils
async def snapchat_marketing_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: 1.0.5

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