How to Use the Mapulus MCP in Google ADK

Power your Google ADK enterprise agents with precise Australian spatial analytics and H3 indexing.

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Connect Mapulus MCP to Google ADK

Create your Vinkius account to connect Mapulus 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 Mapulus with Google ADK

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H3 indexing for Google ADK spatial pipelines

The `get_h3_index` tool converts latitude and longitude coordinates into standardized H3 hexagonal spatial indexes within your Google ADK workflows. This allows your Gemini models to process geographic data in a structured, grid-based format. Once your agent resolves the H3 index, you can query your existing BigQuery spatial datasets using the index as a join key. This turns raw geographic coordinates into clean data points that fit right into your enterprise Google Cloud pipeline.

Demographics for long-context Gemini models

The `get_demographics` tool injects deep Australian demographic profiles directly into the long-context window of your Gemini agent. This allows the model to analyze complex population patterns across thousands of tokens of context. Your Google ADK agent can call `list_data_topics` to see what information is available before requesting specific demographic profiles. This self-directed discovery lets the agent build highly contextual reports without hardcoded prompts.

Automated boundary search and details

The `search_boundaries` tool enables your agent to locate specific Australian statistical areas based on user text queries. The agent can then take the boundary ID and call `get_boundary_details` to retrieve the precise spatial coordinates. You configure this by passing the McpToolset directly into your Google ADK LlmAgent. This MCP integration lets you restrict access to only the specific boundary tools your business logic requires.

Setup guide

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

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Common questions about Mapulus MCP in Google ADK

Initialize McpToolset using your Vinkius HTTP URL and pass it to your LlmAgent tools array. The Gemini model will automatically detect tools like search_suburbs and use them during execution.

Yes, you can use the optional tool names filter in the McpToolset configuration to expose only specific tools. For example, you can limit the agent to get_postcode_data and block access to demographic tools.

Gemini's massive context window allows the agent to hold multiple outputs from get_boundary_details simultaneously. This means your agent can compare several Australian regions and write detailed spatial reports in a single turn.

Absolutely, you can have your agent fetch an H3 index using get_h3_index and then run a BigQuery SQL tool to join that index against your internal enterprise tables. This bridges live Australian spatial analytics with your static data warehouse.

All requests to fetch Australian postcode data or boundaries are routed through an ephemeral V8 sandbox. Your sensitive coordinates never touch persistent storage, and the MCP connection uses a single secure token managed by Vinkius.

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