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How to Use the FRED Tags & Sources — Data Discovery MCP in Mastra AI

Build automated economic data workflows with Mastra AI and live FRED metadata.

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Connect FRED Tags & Sources — Data Discovery MCP to Mastra AI

Create your Vinkius account to connect FRED Tags & Sources — Data Discovery 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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Automated tag discovery in Mastra AI workflows

The `search_tags` tool queries the FRED taxonomy to locate relevant economic classification labels. Mastra AI agents use this tool to discover new data points dynamically when executing multi-step analysis tasks. If a workflow requires inflation metrics, the agent searches for related tags to find alternative datasets. This prevents your automated pipelines from breaking when a specific series gets discontinued.

Multi-tag series filtering with automatic retries

The `get_series_by_tags` tool isolates specific economic datasets by matching combinations of geographic, frequency, and topical tags. Your Mastra workflows use this to narrow down massive lists of series to just the high-signal targets. Mastra's built-in retry engine handles any temporary API rate limits during these queries. If a tag intersection returns too many results, your agent automatically applies exclusion tags to refine the output.

Verify data lineage across 107 sources

The `list_sources` tool retrieves the active directory of all 107 economic data providers feeding into FRED. Your agent runs this tool to cross-reference series origins and ensure compliance with your data quality rules. This allows you to build conditional branching logic based on the data source. For example, your workflow can route BLS data to one pipeline and Census Bureau data to another.

Setup guide

Set up FRED Tags & Sources — Data Discovery 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 FRED Tags & Sources — Data Discovery 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: "fred-tags-sources-data-discovery-mcp-client",
  servers: {
    "fred-tags-sources-data-discovery-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "FRED Tags & Sources — Data Discovery Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to FRED Tags & Sources — Data Discovery tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent FRED Tags & Sources — Data Discovery 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 FRED. 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 FRED Tags & Sources — Data Discovery MCP in Mastra AI

You instantiate the MCP client in your Mastra configuration and spread the tools directly into your agent's tool array. The framework automatically registers the endpoints for use in your workflows.
Yes, Mastra AI features a built-in workflow engine that manages retries and exponential backoff. This ensures your tag queries complete even during periods of heavy FRED API traffic.
Your agent can first search for tags, use conditional logic to select the best matches, and then pass those tags to the series retrieval tool. This entire chain executes autonomously within a single Mastra workflow.
Yes, Mastra AI supports human-in-the-loop approval gates for any tool execution. You can configure the agent to pause and ask for confirmation before pulling large lists of series IDs.
The MCP server operates in an isolated sandbox, handling only public FRED tag strings and source metadata. Your internal workflow states and API keys remain completely private within your Mastra runtime environment.

Start using the FRED Tags & Sources — Data Discovery MCP today

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