How to Use the KDniao MCP in Mastra AI

Q: Can I use KDniao to route shipments in Mastra AI?

By using queryshippingprice and identifycarrier, you can build decision trees that select the best shipping option for every order.

Build resilient logistics workflows in Mastra AI. Automate your shipping pipeline with conditional logic and built-in retries.

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Connect KDniao MCP to Mastra AI

Create your Vinkius account to connect KDniao 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 KDniao with Mastra AI

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Reliable shipping workflows for Mastra AI

Chain `identify_carrier` and `create_electronic_waybill` into a single Mastra workflow. If the carrier auto-detection fails, your agent can branch to a manual selection task automatically. This setup ensures your shipping process doesn't stall. You define the logic, and the agent executes the steps based on the tool outputs.

Automated pickup scheduling in Mastra AI

Use `preorder_pickup` within your agent workflows to handle logistics at scale. You can add retries with exponential backoff if the initial pickup request hits a rate limit. This prevents your operations from breaking during high-volume periods. The agent handles the communication loop until the courier confirms the request.

Manage tracking updates in Mastra AI

Implement `subscribe_tracking` to set up automated notification hooks. Your agent can monitor these updates and trigger follow-up actions if a package is delayed. This keeps your logistics cycle moving without manual intervention. It's built for complex operations that need to handle shipping exceptions gracefully.

Setup guide

Set up KDniao 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 KDniao 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: "kdniao-mcp-client",
  servers: {
    "kdniao-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about KDniao MCP in Mastra AI

Initialize the MCP client in your workflow definition and spread the tool set into your agent tools. You can then call them as steps in your sequence.

Yes, you can configure the Mastra workflow engine to retry tool calls if they fail. This is critical for reliable logistics operations.

By using `query_shipping_price` and `identify_carrier`, you can build decision trees that select the best shipping option for every order.

Enable tool approval in your agent configuration. This requires a human to sign off before the server executes critical actions like scheduling a pickup.

It processes shipment waybill records and delivery status strings. Your data is kept private through the Vinkius sandbox, which wipes state after every task.

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