How to Use the FRED Tags & Sources — Data Discovery MCP in OpenAI Agents SDK

Q: How do I configure the FRED Tags & Sources — Data Discovery MCP Server with OpenAI Agents SDK?

Install the openai-agents package via pip. Create an MCPServerStreamableHttp instance with your endpoint URL and pass it to the mcpservers array in your Agent constructor.

Q: Can my agent filter out unwanted data using FRED Tags & Sources — Data Discovery?

Your agent uses the exclude parameter in getseriesbytags to drop irrelevant series. If you want monthly data but need to avoid quarterly estimates, the agent simply excludes the quarterly tag.

Give your OpenAI Agents SDK production system direct access to official St. Louis Fed macroeconomic tags and sources.

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Connect FRED Tags & Sources — Data Discovery MCP to OpenAI Agents SDK

Create your Vinkius account to connect FRED Tags & Sources — Data Discovery to OpenAI Agents SDK 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 FRED Tags & Sources — Data Discovery with OpenAI Agents SDK

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Combine tags for precise data discovery

The `get_series_by_tags` tool finds exact intersections across the entire FRED database. Your agent passes strings like "usa;gdp" to isolate specific metrics without scraping. You combine include and exclude parameters to drop noisy series. This matters because macroeconomic research requires precision. Instead of guessing series IDs, your agent filters programmatically. Setting `cacheToolsList=True` in your OpenAI setup keeps the routing fast when dealing with hundreds of thousands of potential matches.

Map the 107 official data sources

Accessing the `list_sources` tool pulls the complete registry of institutions feeding the St. Louis Fed. Your agent reads the raw list, from the BLS to the Census Bureau. It maps out where the data originates before pulling any actual numbers. Production systems need provenance. When your handoff agents generate a report, they cite the exact originating institution. The OpenAI tracing dashboard logs every source query, giving you a clear audit trail of where your agent looked.

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The `search_tags` tool lets your agent query the metadata index by text or pull the entire taxonomy. It exposes geographic, topic, and frequency labels directly to the LLM. The agent figures out the right terminology before executing downstream queries. You wire this up using `MCPServerStreamableHttp` in your async context manager. The OpenAI Agents SDK auto-discovers the tools immediately. Built-in guardrails validate the tag combinations so your agent doesn't hallucinate invalid FRED categories.

Setup guide

Set up FRED Tags & Sources — Data Discovery MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all FRED Tags & Sources — Data Discovery tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives FRED Tags & Sources — Data Discovery tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate FRED Tags & Sources — Data Discovery tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="FRED Tags & Sources — Data Discovery Agent",
            instructions="You have access to FRED Tags & Sources — Data Discovery tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about FRED Tags & Sources — Data Discovery MCP in OpenAI Agents SDK

Install the `openai-agents` package via pip. Create an `MCPServerStreamableHttp` instance with your endpoint URL and pass it to the `mcp_servers` array in your Agent constructor.

Yes. You assign this specific toolset to a dedicated research agent. That agent searches tags and lists sources, then hands the verified metadata off to an analysis agent for final processing.

Your agent uses the exclude parameter in `get_series_by_tags` to drop irrelevant series. If you want monthly data but need to avoid quarterly estimates, the agent simply excludes the quarterly tag.

The SDK auto-discovers the MCP tools and handles the schema generation for you. Your agent gets native access to `search_tags` and `list_sources` without you writing custom REST wrappers or handling pagination.

This connection only handles public St. Louis Fed taxonomy records, source lists, and index tags. The V8 Isolate Sandbox destroys the environment after execution, meaning your specific query patterns and combination strategies vanish instantly.

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