How to Use the Uber Eats MCP in Mastra AI

Q: Can Mastra AI handle Uber Eats data privacy?

Yes. To address disputes, the agent uses getorderissues. This tool retrieves sensitive details like refund amounts and timestamps, which your workflow can then use to generate a compliance report.

Q: If I encounter issues with Uber Eats using Mastra AI, where do I check?

You use getorderissues. The workflow retrieves issue descriptions and status updates automatically, allowing you to process the complaint without manual intervention.

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Connect Uber Eats MCP to Mastra AI

Create your Vinkius account to connect Uber Eats 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 Uber Eats with Mastra AI

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Automated Order Progression via MCP Server

Mastra AI lets you build a workflow that handles the entire order lifecycle. First, your agent calls `accept_order` to confirm preparation; then, it can automatically call `mark_order_prep_started`. This sequence ensures no step is missed. If something goes wrong downstream, like needing to cancel an accepted order using `cancel_order`, Mastra AI handles the required reason code and potential platform penalties gracefully.

Handling Operational Failures with Mastra AI

Need robust failure handling? If a restaurant can't fulfill an order, your workflow uses `reject_order`. You specify the reason—like 'item_unavailable' or 'too_busy'—and Mastra AI manages the structured response. This is better than simple rejection. This allows you to build conditional paths: if stock runs low (checked via `update_menu_item_availability`), then reject, and notify an admin.

Full Delivery Monitoring with MCP Server

Monitoring delivery is a multi-step process. Your workflow can start by getting all pending orders using `get_orders`. Then, it checks progress repeatedly via `get_delivery_status` until the order moves to DELIVERED. This repeatable check cycle allows Mastra AI to build reliable monitoring pipelines that handle waiting periods and state changes automatically.

Setup guide

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

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

const result = await agent.generate(
  "List recent Uber Eats 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 Uber Eats. 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 Uber Eats MCP in Mastra AI

You define a multi-step workflow. For instance, an agent checks `get_order` details first; if everything looks good, it proceeds to call `accept_order`. If any step fails (e.g., the order is already accepted), the workflow can retry or notify.

You build complex, failure-proof processes. For example, if a payment attempt fails, your workflow can automatically trigger a second action like calling `get_order_issues` to check for previous refunds before notifying an admin.

Yes. To address disputes, the agent uses `get_order_issues`. This tool retrieves sensitive details like refund amounts and timestamps, which your workflow can then use to generate a compliance report.

Yep. Before any action on menus or orders, you must first run `get_stores` to get the necessary external store ID. This ID is required to scope all subsequent calls.

You use `get_order_issues`. The workflow retrieves issue descriptions and status updates automatically, allowing you to process the complaint without manual intervention.

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