How to Use the Uber Eats MCP in LlamaIndex

Q: How do I check past Uber Eats order statuses with LlamaIndex?

You run getorders and then index the resulting status list. Later, you can ask your agent complex questions about order flow that are answered by referencing the stored data.

Q: Can LlamaIndex help with Uber Eats menu changes?

Yes. After calling getmenus and getting the item structure, indexing those results lets you build a knowledge base for staff training or quality checks regarding product IDs and availability.

Q: What is the best way to track Uber Eats delivery progress with LlamaIndex?

Run getdeliverystatus frequently. Indexing these status updates allows you to create a timeline view that answers questions like, 'When was this order last updated?' based on historical records.

Q: How does Uber Eats inventory management work with LlamaIndex?

You use getmenus and index the output. This allows you to build an internal knowledge base that staff can query for current stock levels or required IDs, even if they don't have live access.

Q: Can I analyze Uber Eats order issues using LlamaIndex?

Definitely. Call getorderissues and index every returned field—issue description, timestamp, refund amount. You can then query patterns in complaints across months of data.

Index Uber Eats data and build knowledge bases with LlamaIndex.

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Connect Uber Eats MCP to LlamaIndex

Create your Vinkius account to connect Uber Eats to LlamaIndex 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 LlamaIndex

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Building a Searchable Order History Index

Don't just execute calls—index them. When you run `get_order_issues`, the output (issue descriptions, timestamps, refunds) becomes searchable knowledge. LlamaIndex stores this data in a vector store. This means later queries aren't limited to 'is it still open?' but can answer things like, 'What were the refund amounts on all issues reported last Tuesday for Uber Eats?'

Tracking Store and Menu Data

Want to know which stores you service? Call `get_stores` once, index that list. Then, if a user asks about store operating hours, LlamaIndex grounds the answer in that indexed data rather than guessing. Similarly, indexing menu results from `get_menus` lets users search across item descriptions and IDs for complex queries.

Automated Status Reporting

Need to report on daily operational flow? Run `get_orders` to get status data (PENDING, ACCEPTED, etc.), index the results, and then combine that with staff logs. Your RAG application can answer questions like, 'How many orders were pending last week vs. this week for Uber Eats?' This turns raw API calls into structured, queryable knowledge.

Setup guide

Set up Uber Eats MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Uber Eats MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Uber Eats tools.",
)
response = await agent.run("List recent Uber Eats data")

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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 LlamaIndex

You run `get_orders` and then index the resulting status list. Later, you can ask your agent complex questions about order flow that are answered by referencing the stored data.

Yes. After calling `get_menus` and getting the item structure, indexing those results lets you build a knowledge base for staff training or quality checks regarding product IDs and availability.

Run `get_delivery_status` frequently. Indexing these status updates allows you to create a timeline view that answers questions like, 'When was this order last updated?' based on historical records.

You use `get_menus` and index the output. This allows you to build an internal knowledge base that staff can query for current stock levels or required IDs, even if they don't have live access.

Definitely. Call `get_order_issues` and index every returned field—issue description, timestamp, refund amount. You can then query patterns in complaints across months of data.

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