Uber Eats MCP Server for LlamaIndex 14 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Uber Eats as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Uber Eats. "
"You have 14 tools available."
),
)
response = await agent.run(
"What tools are available in Uber Eats?"
)
print(response)
asyncio.run(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:
LlamaIndex agents combine Uber Eats tool responses with indexed documents for comprehensive, grounded answers. Connect 14 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
- 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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Uber Eats MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 14 tools from Uber Eats
Why Use LlamaIndex with the Uber Eats MCP Server
LlamaIndex provides unique advantages when paired with Uber Eats through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Uber Eats tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Uber Eats tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Uber Eats, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Uber Eats tools were called, what data was returned, and how it influenced the final answer
Uber Eats + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Uber Eats MCP Server delivers measurable value.
Hybrid search: combine Uber Eats real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Uber Eats to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Uber Eats for fresh data
Analytical workflows: chain Uber Eats queries with LlamaIndex's data connectors to build multi-source analytical reports
Uber Eats MCP Tools for LlamaIndex (14)
These 14 tools become available when you connect Uber Eats to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Uber Eats to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUber Eats + LlamaIndex FAQ
Common questions about integrating Uber Eats MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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Connect Uber Eats to LlamaIndex
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.