How to Use the WHO GHO MCP in Google ADK

Q: What kind of country profiles can WHO GHO provide via Google ADK?

The server provides general snapshots using getwhocountryprofile based on ISO-3 codes. It also allows for detailed, time-series data retrieval on specific health indicators.

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Connect WHO GHO MCP to Google ADK

Create your Vinkius account to connect WHO GHO 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 WHO GHO with Google ADK

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Get a country's basic health profile.

Need an immediate health snapshot? The `get_who_country_profile` tool lets your agent pull data for any ISO-3 code, like 'USA' or 'DEU'. It keeps the interaction simple and direct. This is perfect when you're running complex analyses in BigQuery but need quick context on a foreign nation’s basic health status right away.

Retrieve historical indicator data.

For deep dives, use `get_who_indicator_data`. It returns country-level values for a WHO indicator, breaking down the numbers by year and handling sex disaggregation when available. This is solid, reliable data. Your agent can track changes in mortality rates or life expectancy over long periods. The results are clean enough to dump straight into your existing Google Cloud tables.

Search 2200+ health indicators.

Don't know the code you need? Run `search_who_indicators`. This tool searches through over 2,200 global health indicators from 194 nations. It’s a massive lookup table for your agent. It gives you all the codes necessary to then pull specific data with `get_who_indicator_data` later on. You won't waste time guessing.

Setup guide

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

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Common questions about WHO GHO MCP in Google ADK

You pass the MCP toolset to your LlmAgent within the Google ADK. Your agent then executes the tools, allowing you to access global health data without leaving the Google Cloud environment.

The server provides general snapshots using `get_who_country_profile` based on ISO-3 codes. It also allows for detailed, time-series data retrieval on specific health indicators.

This MCP Server touches epidemiological and demographic statistics from the World Health Organization (WHO). Always treat this structured global health data as confidential information within your BigQuery datasets.

The server indexes over 2,200 indicators covering everything from disease prevalence to environmental health data. You just use `search_who_indicators` to narrow down what you need.

No. The tools are designed to return structured, reliable data that integrates easily into the Gemini ecosystem and BigQuery tables for further reasoning.

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