How to Use the Hyprace F1 Data MCP in Mastra AI

Q: Can I build automated F1 reports using Hyprace F1 Data and Mastra AI?

Yes, you register getraceresults as a tool in your Mastra agent configuration. The framework runs the tool, gathers the race data, and feeds it to your LLM to generate the report.

Q: How do I register this F1 MCP Server with my Mastra setup?

You initialize the client with the server's URL and spread mcpClient.listTools() into your Mastra agent's tools array. The agent immediately knows how to call getdriver and other racing endpoints.

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Setup Hyprace F1 Data with Mastra AI

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Automate post-race summaries with this MCP Server

This MCP Server provides `get_race_results` to feed your automated post-race reporting pipelines. Mastra AI executes this tool immediately after a race weekend concludes, pulling the final finishing order. If the F1 database call fails, Mastra's built-in retry mechanism backs off and tries the query again. Your workflow then triggers `get_standings` to update your internal databases with the new championship order.

Build conditional logic around engine performance

The `list_engine_manufacturers` tool lets your Mastra AI agent audit engine builders across different seasons. You build conditional branches that analyze which teams use which power units. By combining `get_team` with engine builder data, your Mastra workflow flags performance drops. Mastra routes these F1 insights to your notification channels when a specific engine manufacturer experiences a sudden decline in the standings.

Map circuit data using Mastra AI agents

The `list_circuits` tool gives your agent immediate access to track configurations and locations. Mastra AI coordinates the step-by-step retrieval of past race data for a specific track. The agent calls `get_grand_prix` to extract historical weather or layout changes. Because Mastra manages state across these steps, your agent maintains a clean memory of the circuit's history throughout the run.

Setup guide

Set up Hyprace F1 Data 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 Hyprace F1 Data 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: "hyprace-f1-data-mcp-client",
  servers: {
    "hyprace-f1-data-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Hyprace F1 Data Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Hyprace F1 Data tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Hyprace F1 Data transactions"
);
console.log(result.text);

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Common questions about Hyprace F1 Data MCP in Mastra AI

Yes, you register `get_race_results` as a tool in your Mastra agent configuration. The framework runs the tool, gathers the race data, and feeds it to your LLM to generate the report.

Mastra uses its built-in workflow engine to handle retries with exponential backoff. If a heavy query like `list_drivers` or `list_teams` hits a speed bump, the system retries automatically without crashing your agent.

Yes, you use Mastra's human-in-the-loop feature. When the agent uses `get_standings` or `get_qualifying_results` to prepare an update, it pauses for your green light before writing to your database.

You initialize the client with the server's URL and spread `mcpClient.listTools()` into your Mastra agent's tools array. The agent immediately knows how to call `get_driver` and other racing endpoints.

Your queries for driver records and constructor points run in an isolated sandbox. Only public historical F1 data passes through the server, keeping your agent's internal prompt logs completely private.

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