How to Use the Notion MCP in Google ADK

Q: How does the agent handle nested page structures?

The agent uses retrieveblockchildren to fetch first-level blocks. For deeper nested structures, your Python code can run recursive calls to fetch the remaining children.

Power Gemini agents with live Notion data using the Google ADK to analyze databases and update pages directly.

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Connect Notion MCP to Google ADK

Create your Vinkius account to connect Notion to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Notion with Google ADK

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Long-context database analysis with Google ADK

The `query_database` tool pulls raw rows from your workspace so Gemini can analyze thousands of entries at once. Because the Google ADK supports models with massive context windows, your agent can read entire project trackers without running out of memory. You can combine this with BigQuery datasets to cross-reference offline tables with live workspace pages. This MCP Server bridges the gap between your cloud warehouse and your team documentation.

Automatic page generation and block updates

Use `create_page` to generate new project logs directly from your Python scripts. Once a page exists, you can run `append_block_children` to write detailed notes, lists, or headers into the document body. This is ideal for archiving runbooks or daily summaries. The agent reads data from your Google Cloud infrastructure and writes it straight into your team's shared workspace.

Fast workspace search and user directory mapping

The `search` tool locates specific workspace pages or databases by title in seconds. Your agent can then call `retrieve_database` to read the exact schema before attempting to write new records. If you need to assign tasks, the agent can call `list_users` to match email addresses with workspace account IDs. This keeps your automated assignments accurate without hardcoding user values.

Setup guide

Set up Notion MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Notion tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Notion_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Notion tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Notion MCP in Google ADK

Yes, you can pass the Vinkius MCP Server to your Google ADK agent to search databases. The model uses `search` and `query_database` to find and read target workspace pages.

Install the package, define your server parameters with the HTTP endpoint, and register the tools. Your agent will instantly gain access to tools like `create_page` and `update_page`.

Yes, you can use the optional tool names filter in the SDK to restrict exposure. For example, you can expose `retrieve_page` while blocking tools that modify your workspace.

The agent uses `retrieve_block_children` to fetch first-level blocks. For deeper nested structures, your Python code can run recursive calls to fetch the remaining children.

All API requests run through isolated, ephemeral V8 sandboxes that do not store your database records or page blocks. Your credentials are used solely to fetch the requested data and are never cached on Vinkius servers.

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