How to Use the TimezoneDB MCP in Mastra AI

Q: How do I register TimezoneDB MCP tools within a Mastra AI agent?

Instantiate the MCPClient with the server URL and spread the returned tools into your agent's tool configuration. The Mastra AI framework automatically parses the schemas for immediate execution.

Automate conditional timezone workflows and scheduling retries using the TimezoneDB MCP Server for Mastra AI.

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Setup TimezoneDB with Mastra AI

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Conditional Dispatch Workflows in Mastra AI

This MCP Server exposes `get_time_by_location` to feed coordinate data directly into Mastra's step-based workflow engine. Your workflows evaluate the local hour at the target location to route automated notifications. If the agent detects that the local hour falls outside business hours, it branches the execution path. The workflow pauses or queues the message until the destination timezone enters active working hours.

Automated Offset Verification

The `get_dst_status` tool integrates with Mastra's automatic retry logic to verify active offsets before initiating critical system updates. If a transition is underway, the workflow retries the calculation using exponential backoff. The agent processes the DST flag to ensure scheduled tasks execute at the exact intended local moment. This eliminates manual offset calculations from your backend code entirely.

System Health and Global Auditing

Using `list_timezones` and `get_time_by_zone` allows your Mastra agent to audit entire distribution networks and verify local schedules. The agent aggregates the returned offsets to construct a master schedule across multiple countries. To keep your background workflows running smoothly, the agent calls `check_api_status` before initiating large batch updates. If the external status is red, the workflow engine automatically diverts to your local fallback databases.

Setup guide

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

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

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

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Common questions about TimezoneDB MCP in Mastra AI

Instantiate the `MCPClient` with the server URL and spread the returned tools into your agent's tool configuration. The Mastra AI framework automatically parses the schemas for immediate execution.

Yes. You can configure Mastra AI to require manual approval before executing timezone-sensitive actions. The workflow pauses until an administrator approves the agent's proposed offset adjustment.

Mastra's workflow engine uses built-in backoff policies to handle rate limits gracefully. If the API returns a rate-limit error, the step automatically retries without crashing your execution queue.

Yes. The client automatically detects and connects via streamable HTTP or Server-Sent Events (SSE) depending on your hosting setup.

The server runs inside an isolated, ephemeral sandbox that handles authentication at the platform level, keeping your API keys hidden from the agent. Your coordinates and timezone queries are processed in memory and deleted immediately after the step completes.

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