Uber MCP Server for LangChain 9 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Uber through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"uber": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Uber, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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:
LangChain's ecosystem of 500+ components combines seamlessly with Uber through native MCP adapters. Connect 9 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
- 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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Uber MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 9 tools from Uber via MCP
Why Use LangChain with the Uber MCP Server
LangChain provides unique advantages when paired with Uber through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Uber MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Uber queries for multi-turn workflows
Uber + LangChain Use Cases
Practical scenarios where LangChain combined with the Uber MCP Server delivers measurable value.
RAG with live data: combine Uber tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Uber, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Uber tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Uber tool call, measure latency, and optimize your agent's performance
Uber MCP Tools for LangChain (9)
These 9 tools become available when you connect Uber to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Uber to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersUber + LangChain FAQ
Common questions about integrating Uber MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Uber with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
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Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Uber to LangChain
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.