How to Use the Apache Superset MCP in Google ADK

Q: How do I initialize the Apache Superset toolset in Google ADK?

Use McpToolset with your Vinkius HTTP endpoint inside your Python code. Pass this toolset directly to your LlmAgent instance to give Gemini access to the Superset tools.

Q: Can I limit which Apache Superset tools are exposed to my Google ADK agent?

Yes. You can use the optional toolnames filter when setting up your toolset. This lets you expose read-only MCP tools like listcharts while hiding write-heavy or execution tools.

Q: How does Apache Superset handle BigQuery connections when queried by Google ADK?

The agent uses listdatabases to find your BigQuery connection in Superset, then runs queries via executesqlquery. It acts as a bridge, utilizing Superset's existing database drivers.

Q: Does this integration support Gemini's long-context capabilities?

Absolutely. You can pull massive query results using executesqlquery and pass the entire raw dataset directly to Gemini, allowing for deep analysis of business trends.

Feed live Apache Superset BI data directly into Gemini's million-token context using Google ADK.

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Connect Apache Superset MCP to Google ADK

Create your Vinkius account to connect Apache Superset 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 Apache Superset with Google ADK

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Feed raw SQL results into Gemini via Google ADK

Run complex queries against your data warehouse using `execute_sql_query`. The agent fetches raw database rows and drops them straight into Gemini's massive context window for deep reasoning. This setup lets your enterprise agents analyze months of database transactions in one go. You don't have to worry about running out of tokens when pulling large datasets.

Map out your warehouse schema with this MCP Server

Give your agent a clear view of your data infrastructure. By calling `list_databases` and `list_datasets`, the agent discovers what tables and sources are ready for analysis. Once it understands the layout, it can intelligently select the right dataset without hardcoded paths. This is perfect for complex enterprise environments with hundreds of tables.

Extract dashboard metrics for reporting

Keep your team updated by extracting chart details automatically. The agent uses `list_charts` and `get_chart_details` to pull the latest numbers from your business dashboards. It can then cross-reference these metrics with your BigQuery data or generate natural language status reports for your cloud-hosted services.

Setup guide

Set up Apache Superset 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 Apache Superset 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="Apache Superset_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Apache Superset 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 Apache Superset. 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 Apache Superset MCP in Google ADK

Use `McpToolset` with your Vinkius HTTP endpoint inside your Python code. Pass this toolset directly to your `LlmAgent` instance to give Gemini access to the Superset tools.

Yes. You can use the optional `tool_names` filter when setting up your toolset. This lets you expose read-only MCP tools like `list_charts` while hiding write-heavy or execution tools.

The agent uses `list_databases` to find your BigQuery connection in Superset, then runs queries via `execute_sql_query`. It acts as a bridge, utilizing Superset's existing database drivers.

Absolutely. You can pull massive query results using `execute_sql_query` and pass the entire raw dataset directly to Gemini, allowing for deep analysis of business trends.

Your credentials remain securely stored inside your Superset instance. The server communicates with Superset via API tokens, and all data transmission happens over encrypted connections managed by Vinkius.

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