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

Build multi-step data pipelines for LangChain using FRED Tags & Sources — Data Discovery to pull precise economic series.

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Connect FRED Tags & Sources — Data Discovery MCP to LangChain

Create your Vinkius account to connect FRED Tags & Sources — Data Discovery to LangChain 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 LangChain

Ask AI about this MCP

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Claude

Perplexity

Chain economic data into your LangChain agent

Connect your agents to `get_series_by_tags` to fetch specific economic datasets directly into your reasoning chain. By passing tags like 'gdp' or 'inflation', you force the model to pull grounded, numerical series instead of relying on its training weights. Your chain logic dictates the flow. Once the data returns, it moves to the next step of your pipeline for analysis or formatting. It's built for developers who want raw data flowing through their nodes without manual intervention.

Filter and discover sources with LangChain

Use `list_sources` to expose every available data provider within your LangChain environment. This prevents the agent from hallucinating sources by keeping it locked to the 107 official institutions indexed by FRED. Everything happens in real-time. Your agent checks the source list before querying, ensuring the data it retrieves originates from verified entities like the BLS or Census Bureau.

Iterative tag discovery for LangChain

Run `search_tags` to map out the available taxonomy before you build your agent's tool set. This allows the model to understand the structure of the data repository before it executes a retrieval task. It removes guesswork from the agent's logic. You see exactly which tags exist, allowing you to construct more accurate search strings that yield higher quality results for your downstream tasks.

Setup guide

Set up FRED Tags & Sources — Data Discovery MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes FRED Tags & Sources — Data Discovery tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "fred-tags-sources-data-discovery-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent FRED Tags & Sources — Data Discovery transactions"
    })
    print(result["messages"][-1].content)

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Common questions about FRED Tags & Sources — Data Discovery MCP in LangChain

You integrate the tools by passing them to your agent constructor. Once registered, the agent calls `search_tags` to identify relevant identifiers before fetching the actual series. It handles the discovery process based on your prompt requirements.

Yes. Every tool output is formatted for immediate consumption by LangChain chains. You can pipe the data into a prompt template or a secondary analysis tool without extra parsing.

The server remains stateless, but you can maintain context by using the client session object. This ensures your LangChain agent retains its history while querying the API.

The tool returns an empty set. Your agent logic should include a fallback or a request for the user to try broader tags.

This server only accesses public macroeconomic metadata. It does not touch your private documents or user-specific data, and no sensitive information is ever transmitted to the FRED API.

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