How to Use the AfterShip Returns MCP in Mastra AI

Q: How can I build a conditional workflow for AfterShip Returns with Mastra AI?

In your Mastra workflow, use the listreturns tool. Then, loop through the results and use getreturndetails. Mastra's native if/then logic lets you decide whether to call approvereturn based on the details you get back.

Q: Can I require human approval for certain return actions?

Absolutely. Mastra supports requireToolApproval as a feature. You can configure your agent to pause and wait for a person to confirm before it executes a sensitive action like approvereturn for a high-value item.

Automate your entire returns process with resilient, multi-step workflows using Mastra AI and AfterShip Returns.

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Connect AfterShip Returns MCP to Mastra AI

Create your Vinkius account to connect AfterShip Returns 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 AfterShip Returns with Mastra AI

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Fully Automated Return Handling

Build a workflow that kicks off the moment a return is requested. Use `list_returns` to find new pending requests. Mastra's engine can then automatically run `get_return_details` to check the return reason and item value. Based on that data, you apply Mastra's conditional logic. If the item value is below a certain threshold and the reason is 'wrong size,' automatically call `approve_return`. If not, the agent can flag it for manual review. That's real automation.

Resilient Warehouse Check-In

Create a workflow for when returns arrive at the warehouse. An agent uses the `receive_items` tool to log the item's arrival and condition. If the AfterShip API is temporarily down or slow, Mastra's built-in exponential backoff automatically retries the call. You don't have to write any of that retry logic yourself. This makes your warehouse intake process far more reliable. You can even add a step to notify a Slack channel if the retry fails after a certain number of attempts.

Build Complex Reports with Mastra AI

Schedule a nightly Mastra agent to run. It can use `list_returns` to pull all returns from the past 24 hours. The agent can then process this data, maybe calling `get_return_details` for each one to get more granular information for your records. The final step could be sending a summary report to an admin or writing the data to a database for long-term analysis. Mastra handles the scheduling, execution, and error handling for the entire job. This MCP server gives your agent the tools to get the data.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AfterShip Returns. 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.

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Common questions about AfterShip Returns MCP in Mastra AI

In your Mastra workflow, use the `list_returns` tool. Then, loop through the results and use `get_return_details`. Mastra's native `if/then` logic lets you decide whether to call `approve_return` based on the details you get back.

Yes, that's a core feature of Mastra. If a call to a tool like `receive_items` fails, Mastra's engine will automatically retry with exponential backoff. You don't need to write any custom error-handling code for transient network issues.

Install the Mastra MCP package via npm. You'll instantiate a new `MCPClient`, providing your Vinkius server URL. Then, you can list the available tools and add them to your agent's configuration.

Absolutely. Mastra supports `requireToolApproval` as a feature. You can configure your agent to pause and wait for a person to confirm before it executes a sensitive action like `approve_return` for a high-value item.

Your connection is secured by your Vinkius endpoint token, which authenticates every action. This server only handles data for return management, like RMA details and logistics status. The ephemeral nature of the Vinkius sandbox means your data is processed and then immediately discarded.

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