Can I actually book rides through this MCP server?

Yes! Unlike some ride-sharing MCPs that only provide estimates, this server can create actual ride requests via the Lyft API. You can book rides, check status, track driver details, and even cancel — all through AI agent commands. A valid Lyft account with payment method on file is required.

What Lyft API permissions do I need?

You need Client ID and Client Secret from the Lyft Developer Portal with 'Public' or 'Full' access scopes. The client credentials flow (2-legged OAuth) provides access to ride types, cost estimates, ETA estimates, ride requests, and history. For user-specific data, additional scope approval may be needed.

Does this work in all cities where Lyft operates?

Yes, this MCP server works in all cities served by Lyft, primarily across the United States and select Canadian cities. Ride availability depends on your local Lyft service area. The API will return accurate ride types, pricing, and ETAs for any location where Lyft operates.

Lyft MCP Server for Mastra AI 9 tools — connect in under 2 minutes

Name: Lyft
Availability: InStock
Author: Vinkius

docs.mastra.ai/docs/agents/mcp-tools

Built by Vinkius GDPR 9 Tools SDK

Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect Lyft through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

typescript

import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";

async function main() {
  // Your Vinkius token. get it at cloud.vinkius.com
  const mcpClient = await createMCPClient({
    servers: {
      "lyft": {
        url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
      },
    },
  });

  const tools = await mcpClient.getTools();
  const agent = new Agent({
    name: "Lyft Agent",
    instructions:
      "You help users interact with Lyft " +
      "using 9 tools.",
    model: openai("gpt-4o"),
    tools,
  });

  const result = await agent.generate(
    "What can I do with Lyft?"
  );
  console.log(result.text);
}

main();

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* 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:

Mastra's agent abstraction provides a clean separation between LLM logic and Lyft tool infrastructure. Connect 9 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.

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 Mastra AI 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 Mastra AI via MCP

Follow these steps to integrate the Lyft MCP Server with Mastra AI.

Install dependencies

Run npm install @mastra/core @mastra/mcp @ai-sdk/openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.ts and run with npx tsx agent.ts

Explore tools

Mastra discovers 9 tools from Lyft via MCP

Why Use Mastra AI with the Lyft MCP Server

Mastra AI provides unique advantages when paired with Lyft through the Model Context Protocol.

Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add Lyft without touching business code

Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation

TypeScript-native: full type inference for every Lyft tool response with IDE autocomplete and compile-time checks

One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure

Lyft + Mastra AI Use Cases

Practical scenarios where Mastra AI combined with the Lyft MCP Server delivers measurable value.

Automated workflows: build multi-step agents that query Lyft, process results, and trigger downstream actions in a typed pipeline

SaaS integrations: embed Lyft as a first-class tool in your product's AI features with Mastra's clean agent API

Background jobs: schedule Mastra agents to query Lyft on a cron and store results in your database automatically

Multi-agent systems: create specialist agents that collaborate using Lyft tools alongside other MCP servers

Lyft MCP Tools for Mastra AI (9)

These 9 tools become available when you connect Lyft to Mastra AI 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