How to Use the Mapulus MCP in OpenAI Agents SDK

Equip your OpenAI Agents SDK with real-time Australian spatial data and demographic intelligence.

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Connect Mapulus MCP to OpenAI Agents SDK

Create your Vinkius account to connect Mapulus to OpenAI Agents SDK 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 OpenAI Agents SDK

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Mapulus isochrones in OpenAI Agents SDK

The `get_isochrone` tool lets your OpenAI Agents SDK calculate precise travel times for Australian locations directly inside your agent loops. You can feed coordinates to your agent, and it queries drive times without you writing custom routing logic. Since OpenAI agents run with built-in guardrails, you can validate these spatial outputs before passing them to downstream systems. The agent handles the async MCP connection under the hood, making sure your travel-time boundaries are verified on the fly.

Target demographics with statistical boundaries

The `get_demographics` tool exposes highly specific Australian population data directly to your multi-agent workflows. When an agent receives a boundary identifier, it calls this tool to retrieve age, income, and household structures. You can set up specialized agents in the OpenAI Agents SDK to handle different statistical areas. One agent can find the boundary via `search_boundaries` and pass it off to a demographic analyzer agent using native SDK handoffs.

Instant suburb and postcode mapping

The `search_suburbs` tool resolves messy Australian address inputs into clean, standardized suburb names. If your agent gets a raw string from a user, it queries this tool alongside `get_postcode_data` to confirm the location's validity. This setup runs through a single Vinkius endpoint, letting your OpenAI Agents SDK auto-discover the MCP tools with zero manual schema definition. You get full execution tracing on your OpenAI dashboard for each postcode lookup.

Setup guide

Set up Mapulus MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Mapulus tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Mapulus tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Mapulus tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Mapulus Agent",
            instructions="You have access to Mapulus tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Mapulus MCP in OpenAI Agents SDK

Install the packages and define the server stream using MCPServerStreamableHttp with your Vinkius URL. Pass this server object in the mcp_servers list when initializing your Agent. The SDK auto-discovers all tools like get_isochrone and search_suburbs instantly.

Yes, you can route tasks between specialized agents based on the spatial data retrieved. For example, one agent can search boundaries with search_boundaries and hand the result to another agent that calls get_demographics to analyze the area.

You can set cacheToolsList=True in the server parameters to prevent redundant schema lookups. This keeps your agent execution fast when calling tools like get_h3_index repeatedly.

You can define input and output validators directly on your agents to inspect the arguments passed to enrich_location. This ensures your agent does not execute queries with malformed coordinates or invalid Australian postcodes.

No raw personal identifier data is processed or stored; we only query aggregated Australian statistical boundaries. The Vinkius MCP sandbox acts as a zero-trust buffer, ensuring your spatial queries and demographic requests are isolated and ephemeral.

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