How to Use the World Bank Economy MCP in Google ADK

Run enterprise World Bank Economy analyses with Google ADK.

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Connect World Bank Economy MCP to Google ADK

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

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Cross-Reference Economic Metrics via MCP Server

Need to compare multiple economic measures? Use `get_gdp` and then layer in data from `get_inflation`. Since you're running on Google Cloud, your agents can easily take this output and cross-reference it with metrics already sitting in BigQuery. This allows for complex reasoning over massive datasets. Your Long-context Gemini model handles the analysis without needing to manually stitch together multiple API calls.

Automate Trend Spotting with Google ADK

`get_gdp_growth` provides the annual percentage change in GDP, a core metric for spotting trends. You can combine this tool call with `get_economy_indicator` to build multi-year reports. The agent framework supports native integration with Vertex AI and BigQuery, letting you perform advanced statistical modeling immediately after retrieving raw data.

Model Economic Context with MCP Server

To get a full picture of a market, use `get_economy_indicator` for the specific code you need. This tool is your entry point to World Bank’s data pool. The ability to hold 1M+ tokens means that even if your analysis involves dozens of these economic lookups, Gemini won't lose context or forget the initial premise.

Setup guide

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

Get your connection token →

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Common questions about World Bank Economy MCP in Google ADK

You initialize the MCP toolset and pass it to your LlmAgent. Because you're already in the Google Cloud ecosystem, this connection feels natural; the agent treats the external tools like native services.

Absolutely. The framework is built for enterprise agents that need reliability and scale. It supports Stdio and HTTP transports, making it robust enough for mission-critical economic reporting.

It provides core indicators like GDP (current US$), inflation rates via `get_inflation`, and various specialized metrics through the general indicator lookup. All this raw data is ready for BigQuery analysis.

Yes, it's designed to play well within your stack. The agent can fetch an economic value and immediately write or compare that result against a table in BigQuery.

The server deals with quantitative financial metrics, including specific currency values (US$) for GDP and percentage rates for inflation. This is structured economic indicator data.

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