Uber MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Uber as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Uber?"
)
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 MCP Server
What you can do
Connect your AI agents to the Uber platform for seamless ride management and trip planning:
LlamaIndex agents combine Uber tool responses with indexed documents for comprehensive, grounded answers. Connect 9 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.
- Get available ride products (UberX, Black, Comfort) at any location
- Estimate prices across all ride types before booking
- Compare pickup times to choose the fastest option
- View complete trip history with pricing and route data
- Save and manage favorite places (Home, Work, custom locations)
- Autocomplete place searches for accurate pickup/dropoff coordinates
The Uber MCP Server exposes 9 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 to LlamaIndex via MCP
Follow these steps to integrate the Uber 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 9 tools from Uber
Why Use LlamaIndex with the Uber MCP Server
LlamaIndex provides unique advantages when paired with Uber through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Uber tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Uber tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Uber, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Uber tools were called, what data was returned, and how it influenced the final answer
Uber + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Uber MCP Server delivers measurable value.
Hybrid search: combine Uber real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Uber 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 for fresh data
Analytical workflows: chain Uber queries with LlamaIndex's data connectors to build multi-source analytical reports
Uber MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Uber to LlamaIndex via MCP:
add_saved_place
Requires alias name, latitude, and longitude. Optionally include a full address string. The alias can be home, work, or any custom string. Returns the saved place details. Save a new place for the authenticated Uber user
get_place_autocomplete
Requires current user location to bias results. Returns place descriptions and structured address components. Use this to help users select valid pickup/dropoff locations before requesting rides. Autocomplete place predictions for Uber locations
get_price_estimate
Prices are in local currency. Use this to compare costs across different Uber ride types before booking. Get price estimate for an Uber ride between two locations
get_products
) available at the specified latitude/longitude. Returns product IDs, display names, capacity, and descriptions. Use this to see which ride options are available before requesting a ride or price estimate. Get available Uber products at a location
get_ride_estimate
More specific than price estimates as it targets one product. Use this to get exact pricing before requesting a ride. Get detailed ride estimate for a specific Uber product
get_saved_places
Returns place aliases, addresses, and coordinates. Use this to quickly reference saved locations for ride requests or price estimates without typing addresses. List saved places for the authenticated Uber user
get_time_estimate
Use this to compare how quickly different Uber services can pick you up. Lower times mean faster pickups. Get estimated pickup time for Uber at a location
get_trip_history
Returns trip date, start/end locations, product used, distance, and price. Use this to review past rides, calculate expenses, or find a previous trip details. Get trip history for the authenticated Uber user
get_user_profile
Use this to verify authentication and confirm which Uber account is connected. Get the authenticated Uber user profile
Example Prompts for Uber in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Uber immediately.
"Estimate the price for an UberX from my home to the airport at 3pm tomorrow"
"Show me my last 10 Uber trips with total spending"
"What Uber products are available at my current location and how fast can they pick me up?"
Troubleshooting Uber MCP Server with LlamaIndex
Common issues when connecting Uber to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUber + LlamaIndex FAQ
Common questions about integrating Uber 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?
Connect Uber with your favorite client
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Connect Uber to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.