How to Use the Mapulus MCP in LlamaIndex

Q: Can I limit which Mapulus tools my LlamaIndex pipeline can access?

Yes, you can use the allowedtools filter when setting up your MCP tool specification. This lets you expose only searchsuburbs while keeping demographic tools hidden from specific query engines.

Q: How does LlamaIndex handle complex spatial outputs from Mapulus?

The tool outputs from getboundarydetails are structured as clean JSON. LlamaIndex reads this structure, converts it to document nodes, and inserts them directly into your vector index for semantic retrieval.

Q: Why should I index regional coordinates instead of running raw API queries?

LlamaIndex automatically indexes the results of tools like enrichlocation. This means your agent can search through previous spatial queries, reducing API calls and keeping your application fast.

Feed Australian demographic data and boundary details directly into your LlamaIndex vector stores.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Mapulus MCP to LlamaIndex

Create your Vinkius account to connect Mapulus to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Mapulus with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Australian spatial data into LlamaIndex

RAG applications often struggle with live spatial data because static documents don't capture changing boundaries. This MCP Server lets your LlamaIndex pipeline query `get_boundary_details` and instantly index the structural results. Your agent can then retrieve this spatial context to answer complex questions about Australian regions. By loading these live boundaries directly into your vector store, you prevent your model from making up postcode shapes. The agent retrieves verified geographic structures instead of relying on outdated training data.

Ground LlamaIndex queries with Mapulus demographics

When users ask about regional population trends, your LlamaIndex agent can run `get_demographics` to fetch live data. It then indexes these statistics on the fly, making them searchable for subsequent questions. This turns temporary API responses into reusable knowledge. The agent combines these demographic insights with your local markdown files, creating a unified index that answers questions with real-world Australian data.

Build location-aware LlamaIndex RAG pipelines

You can use this MCP server to expose tools like `get_isochrone` directly to your query engines. This allows your system to index travel-time boundaries, meaning your RAG pipeline can filter documents based on actual driving distances. Instead of simple radial searches, your index filters information using true drive times. Your LlamaIndex application reads these calculated polygons to find relevant local data within a specific travel zone.

Setup guide

Set up Mapulus MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Mapulus MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Mapulus tools.",
)
response = await agent.run("List recent Mapulus data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mapulus. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Mapulus now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Mapulus MCP in LlamaIndex

Install the LlamaIndex MCP tool package and initialize the client with your server URL. Wrap it in an `McpToolSpec` to convert tools like `get_postcode_data` into standard LlamaIndex tools.

Yes, you can use the `allowed_tools` filter when setting up your MCP tool specification. This lets you expose only `search_suburbs` while keeping demographic tools hidden from specific query engines.

The tool outputs from `get_boundary_details` are structured as clean JSON. LlamaIndex reads this structure, converts it to document nodes, and inserts them directly into your vector index for semantic retrieval.

LlamaIndex automatically indexes the results of tools like `enrich_location`. This means your agent can search through previous spatial queries, reducing API calls and keeping your application fast.

All coordinate lookups, suburb searches, and demographic queries are processed over SSL and run in isolated V8 sandboxes. Your local LlamaIndex vector store retains the indexed results, but our server never stores or logs the specific coordinates or postcodes you query.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript