How to Use the Namsor MCP in Google ADK

Q: How do I register the Namsor MCP Server inside a Google ADK agent?

Instantiate McpToolset with the Vinkius HTTP server parameters. Pass this toolset into your LlmAgent constructor inside the tools list to give Gemini access to Namsor.

Q: Can I restrict which Namsor tools my Google ADK agent can access?

Yes. Use the optional toolnames filter in the McpToolset configuration to expose only specific tools like predictgender or parsefullname to your agent.

Q: How does Gemini use Namsor data inside Google ADK?

Gemini reads the schema for tools like predictorigin and calls them when it needs to identify the cultural origin of a contact. The model uses the JSON output directly in its reasoning loop.

Analyze names at scale by connecting Google ADK agents directly to Namsor analytics using this MCP Server.

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Connect Namsor MCP to Google ADK

Create your Vinkius account to connect Namsor 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 Namsor with Google ADK

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Process BigQuery name lists with Gemini agents

`parse_full_name` segments bulk name strings pulled directly from your BigQuery datasets into structured data fields. Google ADK feeds these parsed variables into Gemini models to clean up legacy databases. Your agent handles thousands of rows in a single session by using Gemini's long-context window. This process avoids the overhead of manual data entry pipelines.

Map global distribution using this MCP Server

`predict_country` and `predict_diaspora` identify geographic trends in your customer base without requiring IP tracking data. This MCP Server lets your Google ADK agent analyze customer lists to locate where specific diaspora groups reside. You can filter which tools are exposed by defining a `tool_names` list in your toolset configuration. This restriction ensures your agent only accesses the specific geographic tools your project requires.

Segment marketing audiences with Google ADK

`predict_origin` and `predict_ethnicity` allow your Vertex AI models to group leads into demographic cohorts for targeted localization. The Google ADK manages these calls directly, feeding the output back into your enterprise cloud storage. This integration runs over secure HTTP or Stdio transports. You get clean demographic insights without maintaining complex external API integrations.

Setup guide

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

Instantiate `McpToolset` with the Vinkius HTTP server parameters. Pass this toolset into your `LlmAgent` constructor inside the `tools` list to give Gemini access to Namsor.

Yes. Use the optional `tool_names` filter in the `McpToolset` configuration to expose only specific tools like `predict_gender` or `parse_full_name` to your agent.

Yes. The toolset configuration supports both transport layers. For cloud-deployed Google ADK agents, the streamable HTTP transport on Vinkius is the most reliable option.

Gemini reads the schema for tools like `predict_origin` and calls them when it needs to identify the cultural origin of a contact. The model uses the JSON output directly in its reasoning loop.

This MCP Server processes all name strings and origin predictions in memory inside an ephemeral V8 sandbox on Vinkius. No customer data is stored on the server, ensuring compliance with enterprise cloud security policies.

Use it with your favorite AI tools

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