How to Use the Zippopotam.us MCP in Google ADK

Enterprise geo-intelligence for Google ADK agents using BigQuery.

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Connect Zippopotam.us MCP to Google ADK

Create your Vinkius account to connect Zippopotam.us 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 Zippopotam.us with Google ADK

Ask AI about this MCP

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Claude

Perplexity

Find zip codes by city name.

The `lookup_nearby_city` tool allows your agent to find all associated postal codes when given a general city. This is useful for enriching large datasets in BigQuery before running complex queries. Your LlmAgent can use this structured input to focus its reasoning on specific geographical areas, making the overall analysis much tighter.

Lookup location by postal code.

Using `lookup_postcode`, your agent gets exact coordinates and state data for any given zip. This capability lets you pinpoint locations with high accuracy within your enterprise models. Since this runs on Google Cloud, the results are immediately available to power long-context reasoning across 1M+ tokens of data.

MCP Server integration for large scale analysis.

The Google ADK connects your agent directly to Zippopotam.us's MCP Server, giving you location context on demand. This capability is designed for massive workflows running within Vertex AI. It ensures that when your agents need geo-data—whether it’s a simple lookup or complex validation—they pull from a verified source of truth.

Setup guide

Set up Zippopotam.us 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 Zippopotam.us 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="Zippopotam.us_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Zippopotam.us tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Zippopotam.us MCP in Google ADK

Your agent passes the MCP Server toolset to LlmAgent, allowing it to call location lookups (like `lookup_nearby_city`) as part of its reasoning process within BigQuery.

The best way is embedding it into your agent's toolset. You can then restrict exposed tools if you only want the agent to access specific functions, like `lookup_postcode`.

Yes. Beyond simple lookups, your agent can use the geo-data retrieved from this MCP Server to inform large-scale analysis powered by Gemini models.

You can. The tool is managed through the secure framework, meaning your agent always accesses validated and current postal intelligence, critical for production systems.

This MCP Server handles global postal code intelligence, specifically dealing with zip codes, city names, states, coordinates, and location attributes necessary for geo-spatial analysis.

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