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How to Use the NOAA Forecast — US Weather Predictions MCP in Mastra AI

Build weather-aware workflows with Mastra AI. Automatically trigger actions based on NWS forecasts, with built-in retries for reliability.

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Connect NOAA Forecast — US Weather Predictions MCP to Mastra AI

Create your Vinkius account to connect NOAA Forecast — US Weather Predictions 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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Trigger workflows from forecast changes

Use `get_forecast` as a trigger for your Mastra AI agents. Your workflow can monitor a key location, and if the precipitation probability exceeds a threshold, it can automatically dispatch alerts, reschedule jobs, or notify a Slack channel. This isn't just about getting data; it's about acting on it. Because Mastra supports conditional logic, you can build sophisticated chains of events: if `get_hourly_forecast` shows freezing temperatures, then check inventory for de-icing supplies, then create a task for the facilities team.

Build a resilient Mastra AI weather monitor

Weather APIs can be slow during major events. Mastra's automatic retries with exponential backoff mean your agent won't fail just because the NWS servers are under heavy load. Your workflow for pulling `get_grid_data` will patiently retry until it gets the information it needs. This MCP Server is designed for automation. You can set up a workflow to fetch the latest `get_forecast_discussion` every hour, parse it for keywords like 'severe' or 'watch', and escalate to a human if specific conditions are met. Mastra handles the scheduling and reliability so you can focus on the logic.

Get the right data for the job

Different problems require different data. This server gives you options. For a quick summary, `get_forecast` is perfect. For detailed planning, `get_hourly_forecast` gives you 156 hours of granularity. When you need to perform calculations or look for specific patterns, `get_grid_data` delivers the raw numbers. Your Mastra AI agent can use `get_point_metadata` first to translate a simple address into the grid coordinates needed by the other tools, creating a seamless chain of operations.

Setup guide

Set up NOAA Forecast — US Weather Predictions 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. 1

    Install dependencies

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

  2. 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. 3

    Discover and inject tools

    Call mcpClient.listTools() and spread the result into your agent's tools object. All NOAA Forecast — US Weather Predictions tools become native Mastra tools.

  4. 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: "noaa-forecast-us-weather-predictions-mcp-client",
  servers: {
    "noaa-forecast-us-weather-predictions-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent NOAA Forecast — US Weather Predictions transactions"
);
console.log(result.text);

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by NOAA. 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 NOAA Forecast — US Weather Predictions MCP in Mastra AI

Your agent can periodically call `get_forecast` for a location. In your workflow, you can define a condition that if the detailed forecast text contains 'WARNING', it should trigger another tool to send an alert.
The NWS maps the US to a grid system for their forecasts. Use the `get_point_metadata` tool with a latitude/longitude to get the specific grid X/Y and office code; you'll need them for all the other forecast tools.
The underlying NWS API has a public rate limit. Mastra AI's built-in retry logic helps manage this gracefully, but for high-volume use, you should design your workflows to cache results and avoid polling too frequently.
In your agent's configuration, add your Vinkius server URL to the `servers` object in the `MCPClient` constructor. Mastra will then be able to see and use all five of the NOAA forecast tools.
The only data sent is the `latitude` and `longitude` for your query. The request goes through your authenticated Vinkius endpoint to an isolated MCP Server instance that is destroyed after execution. No query history is maintained.

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