Uber Eats MCP Server for Mastra AI 14 tools — connect in under 2 minutes
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect Uber Eats through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.
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Vinkius supports streamable HTTP and SSE.
import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token. get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"uber-eats": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "Uber Eats Agent",
instructions:
"You help users interact with Uber Eats " +
"using 14 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with Uber Eats?"
);
console.log(result.text);
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Uber Eats MCP Server
What you can do
Connect AI agents to the Uber Eats Marketplace API for complete restaurant and delivery management:
Mastra's agent abstraction provides a clean separation between LLM logic and Uber Eats tool infrastructure. Connect 14 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.
- Monitor incoming orders in real-time with status tracking (PENDING → ACCEPTED → PREPARING → READY → DELIVERED)
- Accept or reject orders instantly based on kitchen capacity
- Manage restaurant menus — update prices, availability, descriptions, dietary tags
- Review order details including customer info, items, special instructions, and totals
- Track delivery status with real-time courier GPS location and ETA
- Handle order issues including customer complaints and refund requests
- View store information and configuration across all registered locations
- Mark orders ready for courier pickup when food is prepared
The Uber Eats MCP Server exposes 14 tools through the Vinkius. Connect it to Mastra AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Uber Eats to Mastra AI via MCP
Follow these steps to integrate the Uber Eats MCP Server with Mastra AI.
Install dependencies
Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.ts and run with npx tsx agent.ts
Explore tools
Mastra discovers 14 tools from Uber Eats via MCP
Why Use Mastra AI with the Uber Eats MCP Server
Mastra AI provides unique advantages when paired with Uber Eats through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add Uber Eats without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every Uber Eats tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure
Uber Eats + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the Uber Eats MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query Uber Eats, process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed Uber Eats as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query Uber Eats on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using Uber Eats tools alongside other MCP servers
Uber Eats MCP Tools for Mastra AI (14)
These 14 tools become available when you connect Uber Eats to Mastra AI via MCP:
accept_order
This notifies the customer that the restaurant is preparing their food and triggers courier assignment by Uber Eats. Required before marking order as ready for pickup. Use this to acknowledge incoming orders and begin food preparation. Should be done promptly to maintain good restaurant ratings. Accept a pending Uber Eats order to confirm preparation
cancel_order
This is different from rejection - cancellation happens after acceptance and may result in customer dissatisfaction and potential platform penalties. Requires a cancellation reason. Use only when absolutely necessary (kitchen emergency, safety issue, or unavoidable circumstance). Cancel an already accepted Uber Eats order
complete_order
This should be called after confirmation that the delivery was successful. Closes the order lifecycle and triggers final payment processing. Use this to confirm order completion. Mark an order as fully completed (delivered and finalized)
get_delivery_status
Use this to track delivery progress, answer customer inquiries about their order, or coordinate with couriers. Get real-time delivery tracking status for an Uber Eats order
get_menus
Use this to review menu structure, check which items are available/out of stock, or get menu item IDs needed for availability updates. Get complete menu catalog for a specific Uber Eats restaurant
get_order
Use this to review order contents before accepting, verify special instructions, or prepare items correctly. Get complete details of a specific Uber Eats delivery order
get_order_issues
Returns issue descriptions, timestamps, resolution status, and any refunds issued. Use this to review and address order problems, improve quality, and handle disputes proactively. Get reported issues and complaints for a specific Uber Eats order
get_orders
Can filter by status: PENDING (awaiting restaurant acceptance), ACCEPTED (restaurant confirmed), PREPARING (food being prepared), READY (ready for courier pickup), DELIVERED (completed), CANCELLED, or REJECTED. Returns order IDs, customer info, items ordered, totals, special instructions, and timestamps. Use this to monitor order flow, track pending orders requiring action, or review completed deliveries. List all orders for your Uber Eats restaurants with optional status filter
get_store
Use this to review store configuration, verify delivery settings, or check operational status. Get detailed information about a specific Uber Eats restaurant/store
get_stores
Returns external store IDs, names, addresses, operating status, and business details. Use this tool first to get your store IDs, which are required for all other menu and order management operations. List all restaurants/stores associated with your Uber Eats merchant account
mark_order_prep_started
Updates order status to PREPARING and notifies the customer. Use this to keep customers informed about their order progress and provide accurate delivery time estimates. Mark that food preparation has started for an accepted order
mark_order_ready
This triggers courier dispatch notification. Use this when food is complete and waiting for courier arrival. Couriers will be routed to your location for pickup. Mark order as ready for courier pickup (food is packaged and waiting)
reject_order
The customer is notified and refunded automatically. Provide a reason code: "item_unavailable" (key ingredients out of stock), "too_busy" (kitchen at capacity), "kitchen_closed" (outside operating hours), or "other". Use this when unable to fulfill an order. Excessive rejections may affect restaurant visibility on the platform. Reject a pending Uber Eats order when unable to fulfill it
update_menu_item_availability
Set available=true to mark item as in-stock and orderable, or available=false to mark as out-of-stock. Common use: quickly mark items as unavailable when ingredients run out, then re-enable when restocked. Requires external store ID and menu item ID from get_menus result. Toggle availability status of a menu item (mark as in-stock or out-of-stock)
Example Prompts for Uber Eats in Mastra AI
Ready-to-use prompts you can give your Mastra AI agent to start working with Uber Eats immediately.
"Show me all pending orders and accept them automatically"
"Update the price of 'Margherita Pizza' to R$45.90 and mark it as unavailable"
"Track the delivery status of order #12345 and tell me where the courier is"
Troubleshooting Uber Eats MCP Server with Mastra AI
Common issues when connecting Uber Eats to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpUber Eats + Mastra AI FAQ
Common questions about integrating Uber Eats MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
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Connect Uber Eats to Mastra AI
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.