The Pinterest agent connector is a Python package that equips AI agents to interact with Pinterest 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 Pinterest API v5, enabling access to Pinterest advertising and content management data. Supports reading ad accounts, boards, campaigns, ad groups, ads, board sections, board pins, catalogs, catalog feeds, catalog product groups, audiences, conversion tags, customer lists, and keywords.
Example prompts
The Pinterest connector is optimized to handle prompts like these.
- List all my Pinterest ad accounts
- List all my Pinterest boards
- Show me all campaigns in my ad account
- List all ads in my ad account
- Show me all ad groups in my ad account
- List all audiences for my ad account
- Show me my catalog feeds
- Which campaigns are currently active?
- What are the top boards by pin count?
- Show me ads that have been rejected
- Find campaigns with the highest daily spend cap
Unsupported prompts
The Pinterest connector isn't currently able to handle prompts like these.
- Create a new Pinterest board
- Update a campaign budget
- Delete an ad group
- Post a new pin
- Show me campaign analytics or performance metrics
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Ad Accounts | List, Get, Context Store Search |
| Boards | List, Get, Context Store Search |
| Campaigns | List, Context Store Search |
| Ad Groups | List, Context Store Search |
| Ads | List, Context Store Search |
| Board Sections | List, Context Store Search |
| Board Pins | List, Context Store Search |
| Catalogs | List, Context Store Search |
| Catalogs Feeds | List, Context Store Search |
| Catalogs Product Groups | List, Context Store Search |
| Audiences | List, Context Store Search |
| Conversion Tags | List, Context Store Search |
| Customer Lists | List, Context Store Search |
| Keywords | List, Context Store Search |
Pinterest API docs
See the official Pinterest 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 PinterestConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
connector = connect("pinterest", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
connector = connect("pinterest", workspace_name="<your_workspace_name>")
@tool
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
connector = connect("pinterest", 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)
@PinterestConnector.tool_utils(framework="openai_agents")
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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="Pinterest Assistant", tools=[pinterest_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
connector = connect("pinterest", workspace_name="<your_workspace_name>")
mcp = FastMCP("Pinterest Agent")
@mcp.tool
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = PinterestConnector(
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
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = PinterestConnector(
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
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = PinterestConnector(
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)
@PinterestConnector.tool_utils(framework="openai_agents")
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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="Pinterest Assistant", tools=[pinterest_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = PinterestConnector(
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("Pinterest Agent")
@mcp.tool
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.connectors.pinterest.models import PinterestAuthConfig
connector = PinterestConnector(
auth_config=PinterestAuthConfig(
refresh_token="<Pinterest OAuth2 refresh token.>",
client_id="<Pinterest OAuth2 client ID.>",
client_secret="<Pinterest OAuth2 client secret.>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.connectors.pinterest.models import PinterestAuthConfig
connector = PinterestConnector(
auth_config=PinterestAuthConfig(
refresh_token="<Pinterest OAuth2 refresh token.>",
client_id="<Pinterest OAuth2 client ID.>",
client_secret="<Pinterest OAuth2 client secret.>"
)
)
@tool
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.connectors.pinterest.models import PinterestAuthConfig
connector = PinterestConnector(
auth_config=PinterestAuthConfig(
refresh_token="<Pinterest OAuth2 refresh token.>",
client_id="<Pinterest OAuth2 client ID.>",
client_secret="<Pinterest OAuth2 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)
@PinterestConnector.tool_utils(framework="openai_agents")
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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="Pinterest Assistant", tools=[pinterest_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.pinterest import PinterestConnector
from airbyte_agent_sdk.connectors.pinterest.models import PinterestAuthConfig
connector = PinterestConnector(
auth_config=PinterestAuthConfig(
refresh_token="<Pinterest OAuth2 refresh token.>",
client_id="<Pinterest OAuth2 client ID.>",
client_secret="<Pinterest OAuth2 client secret.>"
)
)
mcp = FastMCP("Pinterest Agent")
@mcp.tool
@PinterestConnector.tool_utils
async def pinterest_execute(entity: str, action: str, params: dict | None = None):
"""Execute Pinterest 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: 0.1.5