Google-Search-Console authentication

This connector is optimized for AI agents. For the data replication connector, see Google Search Console.

This page documents the authentication and configuration options for the Google-Search-Console agent connector.

Hosted mode (most cases)

In hosted mode, create the connector through the Airbyte Agent CLI or API, then execute operations using the CLI, Python SDK, or API. If you need a step-by-step guide, see the developer quickstart.

OAuth

Use the CLI for hosted OAuth connector creation when possible. It opens the hosted setup flow and avoids passing connector secrets through the command line:

airbyte-agent login
airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "google-search-console"
}'

For API-first use cases, create a connector with OAuth credentials directly.

credentials fields you need:

Field Name	Type	Required	Description
client_id	str	Yes	The client ID of your Google Search Console developer application.
client_secret	str	Yes	The client secret of your Google Search Console developer application.
refresh_token	str	Yes	The refresh token for obtaining new access tokens.

replication_config fields you need:

Field Name	Type	Required	Description
site_urls	str	Yes	The URLs of the website property attached to your GSC account. Examples: https://example.com/ or sc-domain:example.com
start_date	str (date)	No	UTC date in the format YYYY-MM-DD. Any data before this date will not be replicated.

Example request:

curl -X POST "https://api.airbyte.ai/api/v1/integrations/connectors" \
  -H "Authorization: Bearer <YOUR_BEARER_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_name": "<WORKSPACE_NAME>",
    "connector_type": "Google-Search-Console",
    "name": "My Google-Search-Console Connector",
    "credentials": {
      "client_id": "<The client ID of your Google Search Console developer application.>",
      "client_secret": "<The client secret of your Google Search Console developer application.>",
      "refresh_token": "<The refresh token for obtaining new access tokens.>"
    },
    "replication_config": {
      "site_urls": "<The URLs of the website property attached to your GSC account. Examples: https://example.com/ or sc-domain:example.com
>",
      "start_date": "<UTC date in the format YYYY-MM-DD. Any data before this date will not be replicated.
>"
    }
  }'

Token

This authentication method isn't available for this connector.

Execution

After creating the connector, execute operations using the CLI, Python SDK, or API. If your Airbyte client can access multiple organizations, set the default organization with airbyte-agent organizations use, include organization_id in AirbyteAuthConfig, or include X-Organization-Id in raw API calls.

CLI

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": "google-search-console"
}'

Describe the connector to see its supported entities and actions:

airbyte-agent connectors describe --json '{
  "workspace": "<your_workspace_name>",
  "name": "google-search-console"
}'

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "google-search-console",
  "entity": "<entity>",
  "action": "<action>",
  "params": {}
}'

Python SDK

The connect() factory returns a fully typed GoogleSearchConsoleConnector 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.google_search_console import GoogleSearchConsoleConnector

connector = connect("google-search-console", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@GoogleSearchConsoleConnector.tool_utils
async def google_search_console_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.google_search_console import GoogleSearchConsoleConnector

connector = connect("google-search-console", workspace_name="<your_workspace_name>")

@tool
@GoogleSearchConsoleConnector.tool_utils
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    """Execute Google-Search-Console 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.google_search_console import GoogleSearchConsoleConnector

connector = connect("google-search-console", 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)
@GoogleSearchConsoleConnector.tool_utils(framework="openai_agents")
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    """Execute Google-Search-Console 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="Google-Search-Console Assistant", tools=[google_search_console_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.google_search_console import GoogleSearchConsoleConnector

connector = connect("google-search-console", workspace_name="<your_workspace_name>")

mcp = FastMCP("Google-Search-Console Agent")

@mcp.tool
@GoogleSearchConsoleConnector.tool_utils
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    """Execute Google-Search-Console 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

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.google_search_console import GoogleSearchConsoleConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = GoogleSearchConsoleConnector(
    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
@GoogleSearchConsoleConnector.tool_utils
async def google_search_console_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.google_search_console import GoogleSearchConsoleConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = GoogleSearchConsoleConnector(
    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
@GoogleSearchConsoleConnector.tool_utils
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    """Execute Google-Search-Console 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.google_search_console import GoogleSearchConsoleConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = GoogleSearchConsoleConnector(
    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)
@GoogleSearchConsoleConnector.tool_utils(framework="openai_agents")
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    """Execute Google-Search-Console 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="Google-Search-Console Assistant", tools=[google_search_console_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.google_search_console import GoogleSearchConsoleConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = GoogleSearchConsoleConnector(
    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("Google-Search-Console Agent")

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

API

curl -X POST 'https://api.airbyte.ai/api/v1/integrations/connectors/<connector_id>/execute' \
  -H 'Authorization: Bearer <YOUR_BEARER_TOKEN>' \
  -H 'X-Organization-Id: <YOUR_ORGANIZATION_ID>' \
  -H 'Content-Type: application/json' \
  -d '{"entity": "<entity>", "action": "<action>", "params": {}}'

Open source mode

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

OAuth

credentials fields you need:

Field Name	Type	Required	Description
client_id	str	Yes	The client ID of your Google Search Console developer application.
client_secret	str	Yes	The client secret of your Google Search Console developer application.
refresh_token	str	Yes	The refresh token for obtaining new access tokens.

Example request:

from airbyte_agent_sdk.connectors.google_search_console import GoogleSearchConsoleConnector
from airbyte_agent_sdk.connectors.google_search_console.models import GoogleSearchConsoleAuthConfig

connector = GoogleSearchConsoleConnector(
    auth_config=GoogleSearchConsoleAuthConfig(
        client_id="<The client ID of your Google Search Console developer application.>",
        client_secret="<The client secret of your Google Search Console developer application.>",
        refresh_token="<The refresh token for obtaining new access tokens.>"
    )
)

Token

This authentication method isn't available for this connector.