How to Use the OceanBase MCP in Google ADK

Q: How do I configure the OceanBase MCP Server for Google ADK?

Use the McpToolset class and provide the server URL. You can then pass this toolset directly to your LlmAgent for immediate access to all cluster management functions.

Q: Can I filter which OceanBase tools my Google ADK agent uses?

Absolutely. You can use the toolnames filter in your McpToolset setup to restrict the agent to only the specific tools you want it to access.

Connect OceanBase to your Google ADK agent to manage distributed databases alongside your BigQuery and Vertex AI workflows.

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Create your Vinkius account to connect OceanBase 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 OceanBase with Google ADK

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Query OceanBase clusters via Google ADK

Use `list_clusters` to pull your infrastructure map into your Gemini-powered agent. This allows your agent to reason about cluster topology in real-time. Because the server connects to the Google ADK, your agent can cross-reference database telemetry with existing cloud metrics. It makes managing distributed systems much faster.

Granular tenant and database inspection

Access your tenant list and database schemas through `list_tenants` and `list_databases`. Your agent can now identify where specific data resides without manual lookup. This level of visibility is necessary for complex enterprise environments. The agent handles the discovery while you focus on the logic that sits on top of your data.

Resource telemetry at your fingertips

Run `get_resource_stats` to feed performance data into your long-context Gemini models. This helps your agent identify bottlenecks across your OceanBase nodes. By leveraging the native toolset, you get structured output that the model can parse immediately. It removes the need for custom parsing scripts.

Setup guide

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

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Common questions about OceanBase MCP in Google ADK

Use the `McpToolset` class and provide the server URL. You can then pass this toolset directly to your `LlmAgent` for immediate access to all cluster management functions.

Absolutely. You can use the `tool_names` filter in your `McpToolset` setup to restrict the agent to only the specific tools you want it to access.

It does. You can feed large amounts of cluster metadata into your agent, allowing for sophisticated analysis of your entire database footprint.

Yes, it runs alongside your existing cloud agents. You can use it to bridge your database operations with services like Vertex AI.

Authentication is handled through a single endpoint token managed by the server. Your database credentials stay locally secured and are not exposed to the agent model directly.

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