How to Use the Baserow MCP in Google ADK

Q: Does Baserow work with Google ADK natively?

Yes, you use the McpToolset to register the server. Your agent then gains the ability to talk to your tables as if they were local functions.

Q: How do I filter rows using Baserow in Google ADK?

You pass your filter criteria into the listrows tool. The server processes the request and sends back only the records that match your query.

Q: Can I use Baserow views with Google ADK?

You can call listviews to see how your data is organized. This helps your agent understand the context of your records before it starts querying.

Q: Is it possible to limit which Baserow tools Google ADK uses?

You can provide a toolnames filter during initialization. This restricts the agent to only the specific operations you want it to perform.

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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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Google ADK integration for Baserow tables

Initialize the `McpToolset` to bring your database tables into the Gemini environment. Your agents see the data structure through `list_tables` and `list_views`. This setup allows your agent to reason over your business logic. It understands how your views are filtered before it attempts to query any records.

Query and update Baserow rows in Google ADK

Call `list_rows` to pull records into your agent's long-context window. It works perfectly with Gemini's ability to process massive amounts of information. When the agent reaches a conclusion, it uses `update_row` to reflect changes back in your database. You can restrict the agent to specific tools by using a name filter.

Real-time schema discovery

Run `list_fields` to let your agent learn the data types of your columns. It maps your database fields to the agent's internal state accurately. This ensures that when your agent triggers a `create_row` action, the JSON structure matches your Baserow table requirements exactly.

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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Common questions about Baserow MCP in Google ADK

Yes, you use the `McpToolset` to register the server. Your agent then gains the ability to talk to your tables as if they were local functions.

You pass your filter criteria into the `list_rows` tool. The server processes the request and sends back only the records that match your query.

You can call `list_views` to see how your data is organized. This helps your agent understand the context of your records before it starts querying.

You can provide a tool_names filter during initialization. This restricts the agent to only the specific operations you want it to perform.

Your database credentials are handled by a single endpoint token in a V8 sandbox. This prevents your agent from seeing anything outside your defined workspace.

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