How to Use the World Bank Education & Health MCP in Google ADK

Q: How do I check educational metrics using World Bank Education & Health and Google ADK?

Use the geteduhealthindicator tool. The results are passed to Gemini, which can then reason about that indicator's meaning in the context of your BigQuery data, going beyond simple retrieval.

Q: Can I get life expectancy using World Bank Education & Health and Google ADK?

Yes. The agent retrieves getlifeexpectancy and embeds that value into a prompt. You can then ask Gemini to compare that global average against the internal data you hold in BigQuery.

Connect World Bank Education & Health metrics to your enterprise BigQuery data using the Google ADK.

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Connect World Bank Education & Health MCP to Google ADK

Create your Vinkius account to connect World Bank Education & Health 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 World Bank Education & Health with Google ADK

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Targeted Data Queries with MCP Server

Need a specific metric? Run `get_edu_health_indicator` to retrieve any World Bank education or health indicator by code. This data feeds directly into your long-context Gemini prompts, allowing the agent to cross-reference it against BigQuery tables. The Google ADK ensures that these retrieved facts—like an indicator value—are immediately available alongside your private enterprise datasets for comprehensive analysis.

Modeling Population Health with Google ADK

Calculate population health trends by pulling `get_life_expectancy` and `get_infant_mortality`. The agent holds both sets of data in its massive context window, letting you run complex calculations that relate life expectancy gaps to mortality risks. This native integration with Google Cloud infrastructure means the analysis doesn't stop at data retrieval; it continues into your existing BigQuery environment.

Linking Finance and Social Data via MCP Server

To gauge development gaps, query `get_health_expenditure` (as a % of GDP) alongside `get_literacy_rate`. The agent can then write complex SQL logic in BigQuery that correlates financial input with social outcome. This capability turns raw global metrics into actionable insights by integrating them seamlessly with your internal corporate data models.

Setup guide

Set up World Bank Education & Health 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 World Bank Education & Health 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="World Bank Education & Health_agent",
    model="gemini-2.0-flash",
    instruction="You have access to World Bank Education & Health tools via MCP.",
    tools=mcp_tools,
)

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Common questions about World Bank Education & Health MCP in Google ADK

Use the `get_edu_health_indicator` tool. The results are passed to Gemini, which can then reason about that indicator's meaning in the context of your BigQuery data, going beyond simple retrieval.

Yes. The agent retrieves `get_life_expectancy` and embeds that value into a prompt. You can then ask Gemini to compare that global average against the internal data you hold in BigQuery.

The server offers five tools: indicators, health expenditure percentage of GDP, infant mortality rate, life expectancy at birth, and adult literacy rate. These metrics give a full picture of global social welfare.

Because it supports long-context reasoning with Gemini models, the agent can execute several tool calls—like getting `get_infant_mortality` and then `get_health_expenditure`—and hold all those facts in memory for one massive analysis.

The server touches aggregate global statistics, including indicators and mortality rates. Since your agent runs on Google Cloud, you can further restrict how this public domain data interacts with your private BigQuery schemas.

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