How to Use the Lyft MCP in Mastra AI

Q: How does Mastra AI manage Lyft tool approvals?

You can enable manual approval for critical tools like requestride or cancelride. This forces the agent to pause until a human confirms the action in your interface.

Orchestrate complex ride workflows in Mastra AI. Build resilient agents that handle booking logic and error recovery.

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Connect Lyft MCP to Mastra AI

Create your Vinkius account to connect Lyft to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Lyft with Mastra AI

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Resilient ride execution workflows

Design multi-step booking sequences that account for network instability. Your Mastra AI agent sequences `get_cost_estimate` before executing `request_ride` to ensure price accuracy. If the booking fails, the framework automatically triggers your retry logic. This prevents partial state issues when handling high-frequency ride requests.

Conditional Lyft ride logic

Implement branching paths based on trip requirements. Your agent checks `get_ride_types` to filter available vehicles and only proceeds if the desired capacity matches. You can define rules where the agent verifies the ride status via `get_ride_details` before confirming the completion of a trip. This keeps your records aligned with the actual service state.

Centralized ride history management

Sync your internal databases with user activity using `get_ride_history`. Your workflow can periodically pull past trip data to generate expense reports or usage summaries. This keeps your application context updated without manual user input. The agent processes the history logs and pushes relevant updates to your backend systems.

Setup guide

Set up Lyft MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Lyft tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

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

const mcpClient = new MCPClient({
  id: "lyft-mcp-client",
  servers: {
    "lyft-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Lyft Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Lyft tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Lyft transactions"
);
console.log(result.text);

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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.

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Common questions about Lyft MCP in Mastra AI

You can enable manual approval for critical tools like `request_ride` or `cancel_ride`. This forces the agent to pause until a human confirms the action in your interface.

Yes, you can build a workflow that monitors for specific conditions and calls `cancel_ride` if necessary. This allows for automated policy enforcement based on your business rules.

It does. Since the server exposes standard tools, you can wrap any Lyft operation in a Mastra retry block to handle transient API issues gracefully.

Provide your endpoint token to the Vinkius-managed server during initialization. The server handles all OAuth handshakes, so your code doesn't need to manage raw secrets.

All ride history and location data are processed in an ephemeral sandbox. The system clears this information after each agent execution, ensuring no trip details persist in your infrastructure.

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