How to Use the World Bank Economy MCP in Mastra AI

Build complex money market workflows with Mastra AI and World Bank Economy data.

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Connect World Bank Economy MCP to Mastra AI

Create your Vinkius account to connect World Bank Economy 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 World Bank Economy with Mastra AI

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Execute Multi-Step Economic Checks

Need a workflow that checks multiple economic conditions? The World Bank Economy MCP Server lets you sequence tool calls. You can check the current `get_inflation` rate first, and if it exceeds a threshold, trigger a second step to analyze `get_gdp_growth`. Mastra AI handles the conditional branching. If one indicator fails or needs further review, the workflow doesn't crash; it retries with different parameters until success.

Automate Indicator Retrieval

Building a monitoring tool? Use `get_economy_indicator` within Mastra AI. The system automatically handles retries and exponential backoff if the World Bank Economy MCP Server is temporarily unavailable. This means your complex workflow for gathering data—say, calling both `get_gdp` and `get_inflation`—will eventually succeed without you needing to write massive retry logic.

Manage Economic Data Failures

When dealing with real-world financial data, things break. Mastra AI's core strength is handling this. If a call to `get_gdp` fails due to an API issue, the workflow doesn't just stop. You can implement human-in-the-loop approval for sensitive economic analysis or have it automatically switch to a backup data source.

Setup guide

Set up World Bank Economy 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 World Bank Economy 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: "world-bank-economy-mcp-client",
  servers: {
    "world-bank-economy-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about World Bank Economy MCP in Mastra AI

You embed the MCP Server into your workflow engine. Mastra AI reads the available tools and executes them sequentially or conditionally, allowing you to build robust, mission-critical economic analysis processes.

The server provides access to key national metrics: GDP values, inflation rates, and various indicators via `get_economy_indicator`. Your workflow uses this raw financial data as its foundation.

While not directly related to payments, you can model it. For example, if an economic check fails (e.g., high inflation), the workflow can trigger a different action or notification based on that `get_inflation` result.

Yes. You build continuous, scheduled workflows in Mastra AI that repeatedly check metrics like annual GDP growth using `get_gdp_growth`. It handles the persistent execution and state management.

The server touches public economic indicators (GDP, inflation rates). Because you are building automated processes around large, non-personal datasets, the data handling remains focused on aggregate financial metrics.

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