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How to Use the Mapulus MCP in Mastra AI

Build reliable Australian location workflows in Mastra AI using this MCP Server.

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Works with every AI agent you already use

…and any MCP-compatible client

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Mastra AI

Connect Mapulus MCP to Mastra AI

Create your Vinkius account to connect Mapulus to Mastra AI 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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Automated spatial workflows in Mastra AI

Chain together complex location tasks like checking boundaries then fetching demographics. If a lookup fails, Mastra handles the retry logic automatically. Use `search_boundaries` as a trigger for your agentic pipeline. It helps your code decide which dataset to pull based on the user's input.

Complex logic with spatial tools

Build conditional branches that verify location data before proceeding. You can gate your agent's actions based on the output of `get_boundary_details`. This keeps your agent on track. If the boundary is outside the service area, the workflow diverts to an error-handling path instead of executing invalid queries.

Human-in-the-loop location tasks

Enable `requireToolApproval` for sensitive operations like `enrich_location`. This ensures a human verifies the data before the agent acts on it. It's perfect for auditing spatial inputs. Your agents stay accountable while moving through multi-step processes.

Setup guide

Set up Mapulus MCP in Mastra AI

Prerequisites

  • Node.js 18+ and a TypeScript project
  • @mastra/mcp + @mastra/core packages
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install dependencies

    Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).

  2. 2

    Configure the MCPClient

    Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

  3. 3

    Discover and inject tools

    Call mcpClient.listTools() and spread the result into your agent's tools object. All Mapulus tools become native Mastra tools.

  4. 4

    Run with any model

    Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts
import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "mapulus-mcp-client",
  servers: {
    "mapulus-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Mapulus Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Mapulus tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Mapulus transactions"
);
console.log(result.text);

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

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Real-time monitoring

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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 Mastra AI

You define the server in your Mastra config with the provided endpoint URL. Once initialized, you spread the tools into your agent's toolset and call them within your workflow steps.
Mastra's workflow engine supports exponential backoff. If `search_boundaries` encounters a timeout, the agent will automatically retry the request based on your defined policy.
Absolutely. You can pipe the output of `get_isochrone` into a secondary tool like `get_demographics` to analyze the population within a travel-time radius.
You can explicitly map which tools an agent has access to in your workflow definition. This keeps your agent's scope focused on relevant tasks like suburb searches.
Your workflow inputs, such as specific coordinates or postcodes, are processed in memory and never persisted in our logs. Security is enforced through stateless request handling.

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