Amazon-Ads

This connector is optimized for AI agents. For the data replication connector, see Amazon Ads.

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

Amazon Ads is Amazon's advertising platform that enables sellers and vendors to promote their products across Amazon's marketplace. This connector provides access to advertising profiles, portfolios, Sponsored Products campaigns (including ad groups, keywords, product ads, targets, and negative keywords/targets), and Sponsored Brands campaigns and ad groups for managing and analyzing advertising campaigns across different marketplaces.

Example prompts

The Amazon-Ads connector is optimized to handle prompts like these.

• List all my advertising profiles across marketplaces
• Show me the profiles for my seller accounts
• What marketplaces do I have advertising profiles in?
• List all portfolios for one of my profiles
• Show me all sponsored product campaigns
• List all ad groups in my SP campaigns
• Show me all keywords in my sponsored product campaigns
• What product ads are currently running?
• Show me all targeting clauses for my campaigns
• List negative keywords across my ad groups
• Show me all sponsored brands campaigns
• List ad groups in my sponsored brands campaigns
• What campaigns are currently enabled?
• Find campaigns with a specific targeting type
• Which ad groups have the highest default bid?
• What keywords are using broad match type?

Unsupported prompts

The Amazon-Ads connector isn't currently able to handle prompts like these.

• Create a new advertising campaign
• Update my campaign budget
• Delete an ad group
• Generate a performance report

Entities and actions

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

Entity	Actions
Profiles	List, Get, Context Store Search
Portfolios	List, Get
Sponsored Product Campaigns	List, Get
Sponsored Product Ad Groups	List
Sponsored Product Keywords	List
Sponsored Product Product Ads	List
Sponsored Product Targets	List
Sponsored Product Negative Keywords	List
Sponsored Product Negative Targets	List
Sponsored Brands Campaigns	List
Sponsored Brands Ad Groups	List

Amazon-Ads API docs

See the official Amazon-Ads API reference.

SDK installation

uv pip install airbyte-agent-sdk

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 AmazonAdsConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

Pydantic AI

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector

connector = connect("amazon-ads", workspace_name="<your_workspace_name>")

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

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

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector

connector = connect("amazon-ads", workspace_name="<your_workspace_name>")

@tool
@AmazonAdsConnector.tool_utils
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Amazon-Ads 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

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector

connector = connect("amazon-ads", 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)
@AmazonAdsConnector.tool_utils(framework="openai_agents")
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Amazon-Ads 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="Amazon-Ads Assistant", tools=[amazon_ads_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector

connector = connect("amazon-ads", workspace_name="<your_workspace_name>")

mcp = FastMCP("Amazon-Ads Agent")

@mcp.tool
@AmazonAdsConnector.tool_utils
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Amazon-Ads 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

from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

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

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AmazonAdsConnector(
    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
@AmazonAdsConnector.tool_utils
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Amazon-Ads 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

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AmazonAdsConnector(
    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)
@AmazonAdsConnector.tool_utils(framework="openai_agents")
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Amazon-Ads 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="Amazon-Ads Assistant", tools=[amazon_ads_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AmazonAdsConnector(
    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("Amazon-Ads Agent")

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

Open source

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

Pydantic AI

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.connectors.amazon_ads.models import AmazonAdsAuthConfig

connector = AmazonAdsConnector(
    auth_config=AmazonAdsAuthConfig(
        client_id="<The client ID of your Amazon Ads API application>",
        client_secret="<The client secret of your Amazon Ads API application>",
        refresh_token="<The refresh token obtained from the OAuth authorization flow>"
    )
)

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

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

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.connectors.amazon_ads.models import AmazonAdsAuthConfig

connector = AmazonAdsConnector(
    auth_config=AmazonAdsAuthConfig(
        client_id="<The client ID of your Amazon Ads API application>",
        client_secret="<The client secret of your Amazon Ads API application>",
        refresh_token="<The refresh token obtained from the OAuth authorization flow>"
    )
)

@tool
@AmazonAdsConnector.tool_utils
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Amazon-Ads 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

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.connectors.amazon_ads.models import AmazonAdsAuthConfig

connector = AmazonAdsConnector(
    auth_config=AmazonAdsAuthConfig(
        client_id="<The client ID of your Amazon Ads API application>",
        client_secret="<The client secret of your Amazon Ads API application>",
        refresh_token="<The refresh token obtained from the OAuth authorization flow>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@AmazonAdsConnector.tool_utils(framework="openai_agents")
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Amazon-Ads 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="Amazon-Ads Assistant", tools=[amazon_ads_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.amazon_ads import AmazonAdsConnector
from airbyte_agent_sdk.connectors.amazon_ads.models import AmazonAdsAuthConfig

connector = AmazonAdsConnector(
    auth_config=AmazonAdsAuthConfig(
        client_id="<The client ID of your Amazon Ads API application>",
        client_secret="<The client secret of your Amazon Ads API application>",
        refresh_token="<The refresh token obtained from the OAuth authorization flow>"
    )
)

mcp = FastMCP("Amazon-Ads Agent")

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

Authentication

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

Version information

Connector version: 1.0.10