How to Use the World Bank Countries MCP in Google ADK

Q: Can I use World Bank Countries metadata when building agents on Google ADK?

Yes, you can. You'll pass the toolset to LlmAgent. The agent then treats it like any other native source of truth for country data.

Act on World Bank Countries data directly from BigQuery with Google ADK.

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Connect World Bank Countries MCP to Google ADK

Create your Vinkius account to connect World Bank Countries 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 Countries with Google ADK

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Integrate World Bank Countries Metadata into Enterprise Pipelines

The `list_countries` tool gives your agent a clean feed of all World Bank countries. Because you're building on Google Cloud, this metadata can flow directly into BigQuery for historical analysis. It resolves ISO codes and regions right out of the box, so your agents don't waste time cleaning up inconsistent country names.

Advanced World Bank Countries Tooling with MCP Server

Need to restrict data by geography? Use `search_regions` via the MCP Server. Your long-context Gemini agent can hold all that geographic reasoning, letting you analyze complex relationships between regions and other datasets. This capability lets your agents filter massive amounts of enterprise data before running a final query.

Structured Income Level Checks for Google ADK

The `search_income_levels` tool checks if a country is HIC, LIC, or something else. This allows your agents to segment and process data streams based on economic status. This structure works perfectly when querying vast datasets already residing in BigQuery.

Setup guide

Set up World Bank Countries 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 Countries 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 Countries_agent",
    model="gemini-2.0-flash",
    instruction="You have access to World Bank Countries tools via MCP.",
    tools=mcp_tools,
)

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Common questions about World Bank Countries MCP in Google ADK

The agent framework allows you to treat this MCP Server as a native tool. You can pass the results of tools like `list_countries` directly into Gemini for reasoning and correlation with your existing BigQuery data.

Yes, you can. You'll pass the toolset to `LlmAgent`. The agent then treats it like any other native source of truth for country data.

This server handles country ISO codes, geographic regions, and income/lending classifications. All these metadata strings are excellent inputs for long-context reasoning in Gemini models.

It does. By combining the tool outputs with BigQuery, your agent can perform highly nuanced analysis, like finding all LIC countries within a specific region.

Since you're building on Google Cloud, the integration is native. The agent uses secure transports (Stdio or HTTP) to guarantee that the metadata is passed reliably.

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