How to Use the Lyft MCP in LlamaIndex

Q: How do I index my Lyft ride history into a LlamaIndex vector store?

Use the getridehistory tool within your LlamaIndex ingestion pipeline to fetch past Lyft trip details. LlamaIndex then parses and embeds this Lyft travel data, allowing you to search past routes and costs semantically.

Q: Can LlamaIndex query live Lyft ETA estimates during a RAG workflow?

Yes. Your LlamaIndex agent can call getetaestimate to retrieve live pickup times, index the response, and use that real-time Lyft data to answer user queries about the fastest ride options.

Q: How does LlamaIndex resolve saved addresses for Lyft bookings?

The LlamaIndex agent queries getlocations to pull your saved Lyft addresses, indexes those coordinates, and references them when executing requestride or updating spots with setlocation.

Q: Can I check the details of an active Lyft ride using LlamaIndex?

Yes. The getridedetails tool retrieves the real-time status of your active Lyft trip, which your LlamaIndex agent can index to provide continuous, grounded updates on your driver's progress.

Index Lyft ride history and live location data directly into your LlamaIndex knowledge bases using this MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Lyft MCP to LlamaIndex

Create your Vinkius account to connect Lyft 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.

GDPR Free for Subscribers

Setup Lyft with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Lyft travel history for LlamaIndex semantic retrieval

Your LlamaIndex agent can call `get_ride_history` to pull your past Lyft travel records and index them for semantic search. This stops your LlamaIndex agent from hallucinating past Lyft trip costs by grounding queries in actual ride data. You get precise answers about where your Lyft went and how much you paid without manually exporting spreadsheets. The LlamaIndex query engine matches your natural language questions against the indexed `get_ride_details` data.

Ground Lyft ride decisions in live LlamaIndex RAG pipelines

Your LlamaIndex agent uses `get_locations` to retrieve your saved Lyft addresses and index them for live query engines. The agent gathers current Lyft availability and stores these coordinates in your LlamaIndex vector index. This MCP Server ensures your LlamaIndex RAG applications are always working with fresh Lyft coordinates. Your agent can check `get_eta_estimate` to compare Lyft wait times and index the results to answer complex scheduling questions.

Update your LlamaIndex Lyft travel context dynamically

Your LlamaIndex agent can execute `set_location` to update your favorite Lyft spots and dynamically feed your vector index. When you need to book, the LlamaIndex agent pulls the exact location IDs from your vector store to run `request_ride`. If plans change, the LlamaIndex agent retrieves the active Lyft ride ID from the index and executes `cancel_ride` to clean up. This keeps your LlamaIndex spatial database perfectly synchronized with your actual Lyft booking state.

Setup guide

Set up Lyft 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 Lyft 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 Lyft tools.",
)
response = await agent.run("List recent Lyft data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lyft. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Lyft now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Lyft MCP in LlamaIndex

Use the `get_ride_history` tool within your LlamaIndex ingestion pipeline to fetch past Lyft trip details. LlamaIndex then parses and embeds this Lyft travel data, allowing you to search past routes and costs semantically.

Yes. Your LlamaIndex agent can call `get_eta_estimate` to retrieve live pickup times, index the response, and use that real-time Lyft data to answer user queries about the fastest ride options.

The LlamaIndex agent queries `get_locations` to pull your saved Lyft addresses, indexes those coordinates, and references them when executing `request_ride` or updating spots with `set_location`.

Yes. The `get_ride_details` tool retrieves the real-time status of your active Lyft trip, which your LlamaIndex agent can index to provide continuous, grounded updates on your driver's progress.

All coordinates, saved location IDs, and trip logs are processed inside a zero-trust, ephemeral V8 isolate. Vinkius does not persist your spatial Lyft data, keeping your LlamaIndex travel queries completely private.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript