Google-Search-Console

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

The Google-Search-Console agent connector is a Python package that equips AI agents to interact with Google-Search-Console 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 Google Search Console API. Provides access to website search performance data including clicks, impressions, CTR, and average position. Supports listing verified sites, sitemaps, and querying search analytics data broken down by date, country, device, page, and query dimensions.

Example prompts

The Google-Search-Console connector is optimized to handle prompts like these.

List all my verified sites in Search Console
Show me the sitemaps for my website
Get search analytics by date for the last 7 days
Show search performance broken down by country
What devices are people using to find my site?
Which pages get the most clicks?
What queries bring the most traffic to my site?
Which country has the highest CTR for my site?
What are my top 10 search queries by impressions?
Compare mobile vs desktop click-through rates
Which pages have the worst average position?
Show me search performance trends over the last month

Unsupported prompts

The Google-Search-Console connector isn't currently able to handle prompts like these.

Submit a new sitemap
Add a new site to Search Console
Remove a site from Search Console
Inspect a URL's index status
Request indexing for a page

Entities and actions

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

Entity	Actions
Sites	List, Get, Context Store Search, Context Store SQL Query
Sitemaps	List, Get, Context Store Search, Context Store SQL Query
Search Analytics By Date	List, Context Store Search, Context Store SQL Query
Search Analytics By Country	List, Context Store Search, Context Store SQL Query
Search Analytics By Device	List, Context Store Search, Context Store SQL Query
Search Analytics By Page	List, Context Store Search, Context Store SQL Query
Search Analytics By Query	List, Context Store Search, Context Store SQL Query
Search Analytics All Fields	List, Context Store Search, Context Store SQL Query

Google-Search-Console API docs

See the official Google-Search-Console API reference.

Interfaces

Use the Google-Search-Console 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": "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": "sites",
  "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 GoogleSearchConsoleConnector 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
LangChain
OpenAI Agents
FastMCP

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.google_search_console import GoogleSearchConsoleConnector

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

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
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>")

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
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>")

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Google-Search-Console Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
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")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Custom tool bodies

When you need custom tool bodies — or a framework without native support — use GoogleSearchConsoleConnector.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.google_search_console import GoogleSearchConsoleConnector

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

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

@agent.tool_plain
@GoogleSearchConsoleConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="google_search_console_inspect",
    docs_tool="google_search_console_read_docs",
)
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@GoogleSearchConsoleConnector.agent_tool(framework="pydantic_ai")
async def google_search_console_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.google_search_console import GoogleSearchConsoleConnector

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

@GoogleSearchConsoleConnector.agent_tool(
    inspect_tool="google_search_console_inspect",
    docs_tool="google_search_console_read_docs",
)
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@GoogleSearchConsoleConnector.agent_tool()
async def google_search_console_inspect():
    return await connector.inspect_connector()

@GoogleSearchConsoleConnector.agent_tool()
async def google_search_console_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 (google_search_console_inspect, google_search_console_read_docs, google_search_console_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 GoogleSearchConsoleConnector.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 GoogleSearchConsoleConnector.agent_tool above.

Pydantic AI
LangChain
OpenAI Agents
FastMCP

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
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
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
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
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
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>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
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>"
    )
)

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
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>"
    )
)

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Google-Search-Console Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
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")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Open source

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

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.

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
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.>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
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.>"
    )
)

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
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.>"
    )
)

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Google-Search-Console Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
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.>"
    )
)

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

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Custom tool bodies

Pydantic AI
from pydantic_ai import Agent
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.>"
    )
)

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

@agent.tool_plain
@GoogleSearchConsoleConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="google_search_console_inspect",
    docs_tool="google_search_console_read_docs",
)
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@GoogleSearchConsoleConnector.agent_tool(framework="pydantic_ai")
async def google_search_console_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

`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.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.>"
    )
)

@GoogleSearchConsoleConnector.agent_tool(
    inspect_tool="google_search_console_inspect",
    docs_tool="google_search_console_read_docs",
)
async def google_search_console_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@GoogleSearchConsoleConnector.agent_tool()
async def google_search_console_inspect():
    return await connector.inspect_connector()

@GoogleSearchConsoleConnector.agent_tool()
async def google_search_console_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 (google_search_console_inspect, google_search_console_read_docs, google_search_console_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

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
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.>"
    )
)

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
from langchain_core.tools import tool
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.>"
    )
)

@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
from agents import Agent, function_tool
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.>"
    )
)

# 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
from fastmcp import FastMCP
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.>"
    )
)

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

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: 1.0.3

Example prompts​

Unsupported prompts​

Entities and actions​

Google-Search-Console API docs​

Interfaces​

CLI​

Python SDK​

Installation​

Usage​

Hosted​

Custom tool bodies​

Legacy alternatives​

Open source​

Custom tool bodies​

Legacy alternatives​

Authentication​

IP allow list​

Version information​

Example prompts

Unsupported prompts

Entities and actions

Google-Search-Console API docs

Interfaces

CLI

Python SDK

Installation

Usage

Hosted

Custom tool bodies

Legacy alternatives

Open source

Custom tool bodies

Legacy alternatives

Authentication

IP allow list

Version information