How to Use the MeteoSource MCP in Mastra AI

Q: What is the best way to manage location IDs in Mastra AI when using MeteoSource?

Store the result of searchweatherplaces in your workflow context. This avoids redundant searches and keeps your agents focused on the correct coordinates.

Build resilient weather workflows with Mastra AI and the MeteoSource MCP Server.

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Connect MeteoSource MCP to Mastra AI

Create your Vinkius account to connect MeteoSource 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 MeteoSource with Mastra AI

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Automated Weather Workflows with Mastra AI

Construct complex logic paths that act on changing weather conditions. Use `get_point_forecast` as a conditional trigger within your workflows to decide when an agent should execute a task. If the forecast predicts rain, your workflow can branch to notify the user or adjust logistics schedules. This happens automatically without manual intervention.

Location Search and Resolution

Use `search_weather_places` to turn string-based location data into machine-readable IDs. This is the first step in any reliable multi-step process. Once you have the ID, you can chain it into `get_nearest_weather_place` or forecast tools. This ensures your agents are operating on verified, precise data.

Self-Healing Agent Integrations

Mastra AI handles retries, but you should start by checking availability. Use `check_api_status` to ensure the server is ready before your agent starts a long-running process. If the status check fails, the workflow can pause or alert the admin. This prevents your agents from wasting cycles on a dead endpoint.

Setup guide

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

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

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

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Common questions about MeteoSource MCP in Mastra AI

Enable the requireToolApproval setting in your client configuration. This forces the agent to pause and ask for your permission before it executes any weather-related tool.

Yes, but you should build a retry loop around the tool calls. Use the `check_api_status` tool at the start of every workflow run to verify the service is healthy.

It provides point forecasts, timezone data, and location search capabilities. These tools give your agents the specific data points needed to make informed, autonomous decisions.

Store the result of `search_weather_places` in your workflow context. This avoids redundant searches and keeps your agents focused on the correct coordinates.

Your endpoint token is managed by Vinkius, and the server environment keeps your credentials isolated. No raw keys are logged during the execution of your agents' workflows.

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