How to Use the Baserow MCP in Google ADK

Q: How does the Google ADK connect to Baserow?

You instantiate an McpToolset in your Python code, pointing it to the Vinkius server URL. Pass that toolset to your LlmAgent, and it will automatically discover all the Baserow tools like createrow and listtables.

Q: How do I restrict which Baserow tools my Google ADK agent can use?

The McpToolset constructor in the Google ADK has a toolnames filter. You can provide a list of allowed function names, like 'listrows', 'getrow', to create a read-only agent.

Connect Gemini to your Baserow data. The Google ADK lets your enterprise agents read and write to your no-code databases on Google Cloud.

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Connect Baserow MCP to Google ADK

Create your Vinkius account to connect Baserow 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 Baserow with Google ADK

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Act on Baserow Data from Your Gemini Agent

This MCP server gives your Google ADK agent a direct line to your Baserow instance. You can build workflows that pull data from BigQuery, process it with a Gemini model, and then use `create_row` or `update_row` to push the results into a Baserow table. It connects your cloud data warehouse to your no-code front-end. Your agent can handle the whole process. It can list all your tables with `list_tables`, find the right one, and then use `list_rows` to check for existing data before deciding whether to create a new entry or update an old one.

Analyze Full Baserow Tables with Gemini

Gemini's huge context window is perfect for working with Baserow. Your agent can call `list_rows` to pull hundreds or thousands of rows into its context at once. Then, it can perform complex analysis or summarization on the entire dataset without needing to paginate or loop. This changes how you work with your data. Instead of asking for a single record with `get_row`, your agent can reason about the relationships between all rows in a table. It's ideal for generating reports or finding trends stored in your Baserow database.

Build Enterprise Agents with a Baserow MCP Server

Use the Google ADK to build agents that understand your business's data structure. The agent can start at the top with `list_workspaces` and drill down to `list_fields` to learn the schema of any table on the fly. No hardcoding means less maintenance. Because you're on Google Cloud, you can tie these Baserow actions into your existing infrastructure. Trigger an agent to `delete_row` based on an event from Pub/Sub, or use a Cloud Function to kick off a workflow that populates a new table. This MCP Server bridges the gap.

Setup guide

Set up Baserow 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 Baserow 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="Baserow_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Baserow tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Baserow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Baserow MCP in Google ADK

You instantiate an `McpToolset` in your Python code, pointing it to the Vinkius server URL. Pass that toolset to your `LlmAgent`, and it will automatically discover all the Baserow tools like `create_row` and `list_tables`.

Absolutely. It has full CRUD access. Your agent can use the `create_row`, `update_row`, and `delete_row` tools to manage records directly in your Baserow database.

The main benefit is the tight integration with Google Cloud and Gemini. You can analyze massive amounts of row data using Gemini's large context window and build agents that connect directly to services like BigQuery and Vertex AI.

The `McpToolset` constructor in the Google ADK has a `tool_names` filter. You can provide a list of allowed function names, like `['list_rows', 'get_row']`, to create a read-only agent.

The server only processes data for active requests, like the contents of a row you're creating or fetching. Your Baserow credentials and data are handled through the secure, ephemeral Vinkius sandbox. Nothing is logged or stored long-term.

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