How to Use the StarRocks MCP in Google ADK

Q: Can Google ADK list materialized views for StarRocks?

Yes, the agent calls listmvs to gather all materialized view definitions. This is critical when maintaining complex data pipelines in StarRocks.

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Setup StarRocks with Google ADK

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See all data sources available.

Run `list_databases` to get a complete inventory of every database. This is your starting point when building StarRocks-connected agents. The agent can then use `list_tables`, letting you map out exactly which tables exist within that pool of databases.

Track data load jobs across the cluster.

Need to know if a nightly ETL job finished? Use `list_jobs` to check on all data loading processes. It tells you the status and completion time for every run. The agent can also track materialized views via `list_mvs`, ensuring your pre-aggregated reports are up to date.

Check cluster health quickly.

Get an immediate overview of node capacity by calling `get_cluster_info`. This provides crucial metrics on the overall health and size of the StarRocks setup. The agent can also check backend nodes using `list_nodes` to verify machine uptime and connectivity across all physical servers.

Setup guide

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

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Common questions about StarRocks MCP in Google ADK

The framework supports connecting the agent's logic directly to StarRocks. You use `execute_query` to run SQL, and the results are passed back into the agent context for further processing.

The tool returns a list of all defined database names. This is essential metadata that lets your agent know where to look next within the StarRocks environment.

You'll receive detailed data on how much space is consumed. The `get_storage_usage` tool gives specific byte counts and volume metrics for accurate capacity planning.

Yes, the agent calls `list_mvs` to gather all materialized view definitions. This is critical when maintaining complex data pipelines in StarRocks.

The server touches table structure metadata, specifically column names and data types. The agent only sees what the tool returns; it doesn't bypass the security layer to access raw records.

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