How to Use the Nationalize MCP in Google ADK

Q: How do I initialize Nationalize in Google ADK?

Install the SDK with pip install google-adk and create an McpToolset pointing to the Vinkius server URL. Pass this toolset into the tools parameter of your LlmAgent instance. The agent will discover the MCP tools automatically.

Q: Can I limit Google ADK to only use the Nationalize tool?

Yes, use the optional toolnames parameter in the McpToolset configuration. Pass a list containing only predictnationality to ensure the agent does not access other tools. This keeps your execution context clean.

Q: Does Nationalize support batch queries in Google ADK?

The predictnationality tool processes names individually to maintain high precision. Your agent can run these queries sequentially or in parallel using the long-context capabilities of Gemini.

Q: How is the name data secured during processing?

The name strings sent to the predictnationality tool are transmitted over encrypted HTTPS connections. Vinkius executes the code in a zero-trust sandbox, ensuring no name data is cached or stored on the hosting server.

Feed name-origin predictions directly to your Gemini enterprise models using the Google ADK toolset.

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Connect Nationalize MCP to Google ADK

Create your Vinkius account to connect Nationalize 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 Nationalize with Google ADK

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Run `predict_nationality` inside Google Cloud

The `predict_nationality` tool analyzes surname strings to output likely country codes and statistical probabilities. This MCP Server lets your Gemini models query the Nationalize API without leaving your secure cloud environment. Surnames provide the highest accuracy for these demographic queries. The tool returns clean structured data that your agent can immediately write to a BigQuery table or use to filter regional datasets.

Google ADK integration with BigQuery pipelines

You configure this MCP connection by importing `McpToolset` and passing the Vinkius HTTP URL to your `LlmAgent`. The setup supports both Stdio and HTTP transports to match your cloud infrastructure. You can restrict exposed tools using the optional `tool_names` filter to keep your agent focused. This prevents Gemini from wasting tokens on irrelevant tools when analyzing large datasets.

Long-context analysis of name lists

Gemini models handle massive token windows, making them perfect for batch-processing name lists. The agent reads thousands of names from your database and calls the `predict_nationality` tool to categorize them. This approach allows you to run deep market analysis on legacy customer databases. The agent processes the statistical outputs to segment your data by probable region of origin.

Setup guide

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

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Common questions about Nationalize MCP in Google ADK

Install the SDK with `pip install google-adk` and create an `McpToolset` pointing to the Vinkius server URL. Pass this toolset into the `tools` parameter of your `LlmAgent` instance. The agent will discover the MCP tools automatically.

Yes, use the optional `tool_names` parameter in the `McpToolset` configuration. Pass a list containing only `predict_nationality` to ensure the agent does not access other tools. This keeps your execution context clean.

The `predict_nationality` tool processes names individually to maintain high precision. Your agent can run these queries sequentially or in parallel using the long-context capabilities of Gemini.

This MCP Server setup supports both Streamable HTTP and Stdio transport protocols. You can choose the transport that matches your Google Cloud hosting environment, whether you run on Cloud Run or Vertex AI.

The name strings sent to the `predict_nationality` tool are transmitted over encrypted HTTPS connections. Vinkius executes the code in a zero-trust sandbox, ensuring no name data is cached or stored on the hosting server.

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