Lyft MCP Server for LangChain 9 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Lyft through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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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({
"lyft": {
"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 Lyft, 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 Lyft MCP Server
What you can do
Connect AI agents to the Lyft platform for complete ride automation:
LangChain's ecosystem of 500+ components combines seamlessly with Lyft through native MCP adapters. Connect 9 tools via 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 types (Lyft, XL, Lux) at any location
- Estimate ride costs across all products before booking
- Compare pickup ETAs to choose the fastest option
- Request rides directly with origin and destination coordinates
- Track active rides with driver info, vehicle details, and real-time status
- Cancel rides when plans change
- View complete ride history with pricing and route data
- Save favorite locations (Home, Work, custom places)
The Lyft 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 Lyft to LangChain via MCP
Follow these steps to integrate the Lyft 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 Lyft via MCP
Why Use LangChain with the Lyft MCP Server
LangChain provides unique advantages when paired with Lyft through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Lyft 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 Lyft queries for multi-turn workflows
Lyft + LangChain Use Cases
Practical scenarios where LangChain combined with the Lyft MCP Server delivers measurable value.
RAG with live data: combine Lyft tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Lyft, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Lyft tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Lyft tool call, measure latency, and optimize your agent's performance
Lyft MCP Tools for LangChain (9)
These 9 tools become available when you connect Lyft to LangChain via MCP:
cancel_ride
Cancellation policies vary based on ride status - cancellations after driver assignment may incur fees. Use this to cancel rides that were booked by mistake or are no longer needed. Cancel an existing Lyft ride request
get_cost_estimate
Prices are in local currency (USD). Use this to compare costs across different Lyft products before booking. Get cost estimate for a Lyft ride between two locations
get_eta_estimate
Use this to compare how quickly different Lyft services can reach you. Lower minutes mean faster pickups. Get estimated arrival times for Lyft at a location
get_locations
Returns location IDs, names, addresses, and coordinates. Use this to quickly reference saved locations for ride requests without typing full addresses. Get saved locations for the Lyft account
get_ride_details
Use this to track your active ride or review past ride details. Get details of a specific Lyft ride
get_ride_history
Returns ride date, status, origin/destination, ride type, driver, and cost. Use this to review past rides, calculate expenses, or find previous trip details. Get ride history for the authenticated Lyft account
get_ride_types
) available at the specified latitude/longitude. Returns ride type IDs, display names, capacity, and descriptions. Use this to see which ride options are available before requesting price or time estimates. Get available Lyft ride types at a location
request_ride
Requires ride type ID (from get_ride_types), origin coordinates, and destination coordinates. Optionally include pickup/dropoff addresses for clarity. Returns the ride ID and status. Use this to book a ride after confirming price and availability. Request a new Lyft ride
set_location
Requires location ID, latitude, and longitude. Optionally include a display name. The location ID can be home, work, or any custom string. Returns the saved location details. Use this to manage your favorite pickup/dropoff spots. Save or update a location for the Lyft account
Example Prompts for Lyft in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Lyft immediately.
"Get me a price estimate from JFK Airport to Times Square for a Lyft XL"
"Book me a Lyft from my home to San Francisco International Airport"
"Show me my last 20 Lyft rides and total spending"
Troubleshooting Lyft MCP Server with LangChain
Common issues when connecting Lyft to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLyft + LangChain FAQ
Common questions about integrating Lyft 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 Lyft with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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 Lyft to LangChain
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.