Lyft 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 Lyft as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Lyft. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Lyft?"
)
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 Lyft MCP Server
What you can do
Connect AI agents to the Lyft platform for complete ride automation:
LlamaIndex agents combine Lyft 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 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 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 Lyft to LlamaIndex via MCP
Follow these steps to integrate the Lyft 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 Lyft
Why Use LlamaIndex with the Lyft MCP Server
LlamaIndex provides unique advantages when paired with Lyft through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Lyft tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Lyft tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Lyft, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Lyft tools were called, what data was returned, and how it influenced the final answer
Lyft + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Lyft MCP Server delivers measurable value.
Hybrid search: combine Lyft real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Lyft 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 Lyft for fresh data
Analytical workflows: chain Lyft queries with LlamaIndex's data connectors to build multi-source analytical reports
Lyft MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Lyft to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Lyft to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLyft + LlamaIndex FAQ
Common questions about integrating Lyft 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 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 LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.