How to Use the HERE Mobility MCP in Mastra AI

Q: How can I use Mastra AI and HERE Mobility to handle routing errors?

Mastra AI is built for this. You can wrap your discovertrips call in a try/catch block within your workflow. If it fails, Mastra's built-in exponential backoff can retry, or your logic can fall back to a different tool like searchmultimodaltrips.

Build resilient transit workflows with Mastra AI. Automate trip planning and notifications with HERE Mobility's reliable routing tools.

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Connect HERE Mobility MCP to Mastra AI

Create your Vinkius account to connect HERE Mobility 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 HERE Mobility with Mastra AI

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Create Fault-Tolerant Trip Planners

Public transit data can be messy. Mastra AI's workflow engine lets you build trip planners that don't fail on the first error. You can set up a workflow that first tries `get_stations_by_name`. If that fails or returns no results, it can automatically retry with a broader `get_nearby_stations` call using coordinates. This logic extends to route planning. Your agent can call `discover_trips` and validate the result. If the trip involves too many transfers, your Mastra workflow can automatically trigger a second call to `search_multimodal_trips` to look for a better alternative that includes biking.

Automate Alerts with Mastra AI

Use Mastra AI to build agents that monitor transit and act on changes. A workflow could periodically call `get_schedule` for a user's home station. If a specific train is delayed or cancelled, the workflow's conditional logic can trigger a notification to the user's phone. You can also chain tools for smarter alerts. For example, if `get_route_details` shows a disruption on a planned route, your Mastra agent could automatically run a new `discover_trips` search to find an alternate route and suggest it to the user.

Complex Workflows with This MCP Server

Go beyond simple lookups. Build a "commute readiness" agent. Your Mastra AI workflow can start by getting the user's saved work location, find the nearest station with `get_nearby_stations`, check the schedule with `get_schedule`, and then plan the full journey with `discover_trips`. With Mastra's human-in-the-loop feature, you can even require approval. The agent can present the planned trip, and if the user rejects it, the workflow can branch to find another option. This is the power of a real workflow engine connected to an MCP server.

Setup guide

Set up HERE Mobility 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 HERE Mobility 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: "here-mobility-mcp-client",
  servers: {
    "here-mobility-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent HERE Mobility 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 HERE Mobility. 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 HERE Mobility MCP in Mastra AI

Mastra AI is built for this. You can wrap your `discover_trips` call in a try/catch block within your workflow. If it fails, Mastra's built-in exponential backoff can retry, or your logic can fall back to a different tool like `search_multimodal_trips`.

Absolutely. You'd set up a recurring Mastra AI workflow for each subscriber. It would use `get_schedule` and `get_route_details` from the HERE Mobility MCP server to check their commute and trigger alerts based on your custom logic.

Reliability for complex jobs. Mastra AI's workflow engine lets you chain HERE Mobility tools with conditional logic, retries, and fallbacks. This means you can build automated transit agents that are much more resilient to real-world data issues.

Yes, the HERE Mobility tools are backed by a global dataset. Your Mastra AI agent can plan trips in any city covered by the HERE Transit API just by passing the right coordinates to tools like `get_nearby_stations` and `discover_trips`.

Your data is kept private. The HERE Mobility server only sees the immediate inputs for a tool call, like coordinates for `get_stations` or a station ID for `get_schedule`. Vinkius runs the MCP server in a zero-trust, ephemeral sandbox, so none of your workflow's state or data is ever logged or stored.

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