How to Use the Knoema MCP in Mastra AI

Q: How do I initialize Knoema with Mastra AI?

Install the latest package and use new MCPClient(). Pass the MCP Server URL, call mcpClient.listTools(), and spread them into your agent's toolset.

Q: Can I build conditional workflows based on Knoema dataset updates in Mastra AI?

You definitely can. Set a workflow to check getlatestdatasetdata and trigger a database update only if the timestamp shows new macroeconomic figures.

Q: Do I need to manually map dataset regions?

No, you pass listdatasetregions to your agent. It will automatically fetch the supported geographic boundaries and use them to format its subsequent API calls.

Build resilient economic data pipelines by connecting Knoema to your Mastra AI workflows.

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Connect Knoema MCP to Mastra AI

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Setup Knoema with Mastra AI

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Build Resilient Pipelines in Mastra AI

The `search_data_series` tool allows your agent to programmatically locate specific global indicators across thousands of datasets. Mastra AI takes this capability and wraps it in a workflow engine that handles network timeouts automatically when talking to the MCP Server. If a query fails, the framework's exponential backoff kicks in before retrying. You can build a branch that falls back to `search_datasets` if the initial granular search returns zero results, keeping your data pipeline moving without manual intervention.

Audit Economic Data Parameters

The `get_dataset_metadata` tool pulls the exact structure of a dataset so your workflow knows how to query it. Your Mastra AI agent reads this schema to determine which dimensions are mandatory before attempting a bulk download. You chain this with `list_data_topics` and `list_data_units` to validate the incoming numbers. If the workflow expects percentages but the unit returns as USD, the agent catches the mismatch and routes the task to a human for approval.

Automate Routine Data Extraction

The `get_latest_dataset_data` tool retrieves the most recent figures for any tracked indicator. You schedule a Mastra AI workflow to run this every morning, pulling fresh statistics into your internal database. For historical backfilling, `get_data_series` accepts specific mnemonics to pull decades of records at once. The framework manages the pagination and rate limits, ensuring your application gets every data point without crashing the process halfway through.

Setup guide

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

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

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

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Common questions about Knoema MCP in Mastra AI

Install the latest package and use `new MCPClient()`. Pass the MCP Server URL, call `mcpClient.listTools()`, and spread them into your agent's toolset.

You definitely can. Set a workflow to check `get_latest_dataset_data` and trigger a database update only if the timestamp shows new macroeconomic figures.

The framework automatically applies exponential backoff and retries the request. You do not have to write custom retry logic for the connection.

No, you pass `list_dataset_regions` to your agent. It will automatically fetch the supported geographic boundaries and use them to format its subsequent API calls.

This MCP Server processes public economic indicators and global statistics. Your queries pass through a V8 Isolate Sandbox that destroys itself after execution, leaving zero trace of your statistical research.

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