How to Use the Weatherbit MCP in Mastra AI

Q: How do I build a multi-step process with Weatherbit and Mastra AI?

You define the workflow graph. For example, if getairquality shows high PM2.5, your agent can automatically trigger a second call to check for local severe weather alerts using getweatheralerts.

Build complex weather analysis workflows with Mastra AI and the Weatherbit MCP Server.

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Connect Weatherbit MCP to Mastra AI

Create your Vinkius account to connect Weatherbit 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 Weatherbit with Mastra AI

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Perplexity

Handling Complex Forecast Logic via MCP Server

You can build a multi-step workflow: first, check `get_forecast_daily` for general trends. If precipitation probability is high, the agent automatically retries by calling `get_forecast_hourly_by_city` to get specific timings and wind data. This conditional branching handles complex user requests gracefully. The workflow engine manages the calls—it doesn't just run one tool; it chains them based on intermediate results.

Running Weatherbit through Mastra AI

Need to know if a severe weather event (from `get_severe_weather`) is impacting a location that also has poor air quality? Your Mastra workflow can execute both tools sequentially. The agent then combines the two reports into one comprehensive failure analysis. This is ideal for disaster prep applications where multiple data points must be cross-referenced before alerting an administrator.

Analyzing Historical Data with Mastra AI

The workflow can check past weather records using `get_historical_weather` to establish a baseline. Then, it compares those 30 years of data against current live conditions from `get_current_weather`. This comparison logic is the job of your agent framework. It's not just fetching data; it's running an entire analysis: 'How does today compare to the average Tuesday in October?'

Setup guide

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

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

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

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Common questions about Weatherbit MCP in Mastra AI

You define the workflow graph. For example, if `get_air_quality` shows high PM2.5, your agent can automatically trigger a second call to check for local severe weather alerts using `get_weather_alerts`.

It covers everything: air quality metrics (PM2.5, O3), current conditions by city name (`get_current_weather_by_city`), and hourly forecasts for up to 16 days.

Yes. If a forecast call fails due to bad coordinates, your framework can automatically retry the request using exponential backoff or switch to an alternative tool like `get_current_weather`.

The server touches geographic coordinates and specific pollutant concentrations. Because Mastra manages the transport, you control when and how this sensitive weather data is accessed and processed within your application's boundaries.

You can trigger it manually, or set up an event listener. For example, when a user inputs a city name, the flow starts by calling `get_forecast_daily_by_city`.

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