How to Use the Haversine Distance Engine MCP in Mastra AI

Q: How do I add the Haversine Distance Engine to Mastra AI?

Install @mastra/mcp@latest. Create a new MCPClient instance and pass the server URL. Then spread the tools into your agent configuration.

Add precise geospatial math to your Mastra AI workflows without writing custom formulas.

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Connect Haversine Distance Engine MCP to Mastra AI

Create your Vinkius account to connect Haversine Distance Engine 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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Setup Haversine Distance Engine with Mastra AI

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Claude

Perplexity

Automate Logistics Workflows

The `calculate_distance` tool plugs right into your Mastra AI pipelines. Feeding it two coordinate pairs returns the exact distance using the Haversine formula. This setup is perfect for delivery routing or fleet management. Mastra handles the execution logic automatically. If an API call fails upstream, the framework retries the step. Once the coordinates are ready, the agent calculates the mileage and decides the next action in your workflow based on the result.

Mastra AI Conditional Branching

Running `calculate_distance` allows your agent to make decisions based on physical proximity. Finding a distance under ten miles means the workflow can dispatch a local courier. Exceeding fifty miles triggers a freight request. Setting this up takes one line of code. You initialize the MCPClient with your server URL, call `listTools()`, and spread them into your agent. The framework auto-detects the transport layer and handles the rest.

Human-in-the-Loop Routing

Using `calculate_distance` inside a high-stakes workflow requires precision. You can require human approval before the agent acts on the distance data. Mastra pauses the execution until an admin gives the green light. This MCP Server runs fast and predictably. Developers get reliable kilometers and miles every time. That consistency is crucial when building autonomous systems that handle real-world logistics.

Setup guide

Set up Haversine Distance Engine 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

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

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Haversine Distance Engine tools become native Mastra tools.
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: "haversine-distance-engine-mcp-client",
  servers: {
    "haversine-distance-engine-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Haversine Distance Engine MCP in Mastra AI

Install `@mastra/mcp@latest`. Create a new `MCPClient` instance and pass the server URL. Then spread the tools into your agent configuration.

Yes. The engine is stateless and idempotent. If a network hiccup causes Mastra to retry the step, you get the exact same distance calculation safely.

You certainly can. The agent grabs the mileage output from the tool and evaluates it against your workflow rules. It routes the task based on that exact number.

It auto-detects the right connection. It uses Streamable HTTP or SSE depending on what the server endpoint supports.

We process your coordinates in an isolated V8 sandbox that gets destroyed after execution. No GPS data is logged, saved, or used for training. The inputs vanish the moment the calculation finishes.

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