How to Use the Uber MCP in LlamaIndex

Q: How does Uber use LlamaIndex for price estimates?

LlamaIndex indexes the output of getpriceestimate. This means if you're reviewing a report, your agent can pull up historical cost ranges and compare them semantically to current estimated prices.

Q: Can Uber use LlamaIndex for ride history lookups?

Yes. Calling gettriphistory feeds the raw trip data into the vector store. You can then query that index later—for instance, 'Show me all trips over 10 miles in Q3'—without needing to re-run the API call.

Q: Does LlamaIndex help with Uber user profile management?

While the server provides the data via getuserprofile, LlamaIndex makes that information queryable alongside other documents, letting you ground context-specific answers about your connected account.

Turn Uber API results into knowledge graphs with LlamaIndex.

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Connect Uber MCP to LlamaIndex

Create your Vinkius account to connect Uber 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 with LlamaIndex

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Creating a Knowledge Base from MCP Server

LlamaIndex lets you index live data. When your agent calls `get_trip_history`, the full details—like product used, distance, and price for that specific trip date—become searchable knowledge. You can then query past sessions to answer questions grounded in actual API output.

Understanding Availability via MCP Server

Want to know what ride options are available at a new spot? Use `get_products` and index those results. Your RAG application can then take that indexed list of product IDs, capacities, and descriptions and answer complex questions like, 'What's the largest vehicle available near my current location?'

Managing Saved Places for Uber

You don't have to just read saved places; you can search them. Indexing results from `get_saved_places` means your knowledge base knows not only the alias and address, but also how that location relates to other stored data or documents.

Setup guide

Set up Uber 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 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 tools.",
)
response = await agent.run("List recent Uber 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. 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 MCP in LlamaIndex

LlamaIndex indexes the output of `get_price_estimate`. This means if you're reviewing a report, your agent can pull up historical cost ranges and compare them semantically to current estimated prices.

Yes. Calling `get_trip_history` feeds the raw trip data into the vector store. You can then query that index later—for instance, 'Show me all trips over 10 miles in Q3'—without needing to re-run the API call.

It turns your stored locations into searchable facts. Instead of just listing them, you can ask a question like, 'Which saved place is closest to my job?' and the index finds the answer instantly.

It handles location coordinates (latitude/longitude), product details from `get_products`, and structured financial records derived from your trip history. The core output is always indexable text.

While the server provides the data via `get_user_profile`, LlamaIndex makes that information queryable alongside other documents, letting you ground context-specific answers about your connected account.

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