Skip to main content

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.

EntityActions
ProfilesList, Get, Context Store Search, Context Store SQL Query
PortfoliosList, Get
Sponsored Product CampaignsList, Get
Sponsored Product Ad GroupsList
Sponsored Product KeywordsList
Sponsored Product Product AdsList
Sponsored Product TargetsList
Sponsored Product Negative KeywordsList
Sponsored Product Negative TargetsList
Sponsored Brands CampaignsList
Sponsored Brands Ad GroupsList

Amazon-Ads API docs​

See the official Amazon-Ads API reference.

Interfaces​

Use the Amazon-Ads 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": "amazon-ads"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "amazon-ads",
  "entity": "profiles",
  "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 AmazonAdsConnector 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.amazon_ads import AmazonAdsConnector

connector = connect("amazon-ads", 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 AmazonAdsConnector.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.amazon_ads import AmazonAdsConnector

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

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

@agent.tool_plain
@AmazonAdsConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="amazon_ads_inspect",
    docs_tool="amazon_ads_read_docs",
)
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@AmazonAdsConnector.agent_tool(framework="pydantic_ai")
async def amazon_ads_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.amazon_ads import AmazonAdsConnector

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

@AmazonAdsConnector.agent_tool(
    inspect_tool="amazon_ads_inspect",
    docs_tool="amazon_ads_read_docs",
)
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@AmazonAdsConnector.agent_tool()
async def amazon_ads_inspect():
    return await connector.inspect_connector()

@AmazonAdsConnector.agent_tool()
async def amazon_ads_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 (amazon_ads_inspect, amazon_ads_read_docs, amazon_ads_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 AmazonAdsConnector.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 AmazonAdsConnector.agent_tool above.

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 {})

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.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>"
    )
)

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.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>"
    ),
    region="<The Amazon Ads API endpoint URL based on region:
- NA (North America): https://advertising-api.amazon.com
- EU (Europe): https://advertising-api-eu.amazon.com
- FE (Far East): https://advertising-api-fe.amazon.com
>"
)

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 AmazonAdsConnector.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.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>"
    ),
    region="<The Amazon Ads API endpoint URL based on region:
- NA (North America): https://advertising-api.amazon.com
- EU (Europe): https://advertising-api-eu.amazon.com
- FE (Far East): https://advertising-api-fe.amazon.com
>"
)

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

@agent.tool_plain
@AmazonAdsConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="amazon_ads_inspect",
    docs_tool="amazon_ads_read_docs",
)
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@AmazonAdsConnector.agent_tool(framework="pydantic_ai")
async def amazon_ads_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.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>"
    ),
    region="<The Amazon Ads API endpoint URL based on region:
- NA (North America): https://advertising-api.amazon.com
- EU (Europe): https://advertising-api-eu.amazon.com
- FE (Far East): https://advertising-api-fe.amazon.com
>"
)

@AmazonAdsConnector.agent_tool(
    inspect_tool="amazon_ads_inspect",
    docs_tool="amazon_ads_read_docs",
)
async def amazon_ads_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@AmazonAdsConnector.agent_tool()
async def amazon_ads_inspect():
    return await connector.inspect_connector()

@AmazonAdsConnector.agent_tool()
async def amazon_ads_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 (amazon_ads_inspect, amazon_ads_read_docs, amazon_ads_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 AmazonAdsConnector.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 AmazonAdsConnector.agent_tool above.

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>"
    ),
    region="<The Amazon Ads API endpoint URL based on region:
- NA (North America): https://advertising-api.amazon.com
- EU (Europe): https://advertising-api-eu.amazon.com
- FE (Far East): https://advertising-api-fe.amazon.com
>"
)

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 {})

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: 1.0.10

Was this page helpful?