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

Index live economic series into your LlamaIndex knowledge base with FRED Tags & Sources — Data Discovery.

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Connect FRED Tags & Sources — Data Discovery MCP to LlamaIndex

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

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Ground LlamaIndex queries in real-time data

Use `get_series_by_tags` to pull specific datasets and feed them into your LlamaIndex vector store. Instead of static documents, your index now contains live macroeconomic series that update whenever your agent runs the tools. This creates a queryable base of facts. When you ask a question, the agent retrieves the actual data points from FRED and uses them to ground its response.

Browse FRED sources within LlamaIndex

Call `list_sources` to index the full directory of 107 FRED data providers. This gives your agent a complete map of available information sources before it performs a semantic search. It saves tokens by letting the model query the index for source availability rather than guessing. You get a clearer picture of where your data comes from.

Index tag metadata for LlamaIndex

Utilize `search_tags` to catalog the available categories and labels. By indexing these tags, you make your entire knowledge base searchable by topic, geography, or frequency. This makes discovery faster. Your agent can find the right series by querying the indexed tags, leading to more relevant data retrieval for your RAG pipeline.

Setup guide

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

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all FRED Tags & Sources — Data Discovery MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to FRED Tags & Sources — Data Discovery tools.",
)
response = await agent.run("List recent FRED Tags & Sources — Data Discovery data")

Get your connection token →

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 LlamaIndex

You use the McpToolSpec to wrap the server tools and inject them into your FunctionAgent. This allows LlamaIndex to treat FRED metadata as part of your knowledge base.

Absolutely. You can take the output from these tools and embed them directly into your existing index structures for semantic retrieval.

Yes. You define the tags you want to search, and the tool returns only the series that match your specific criteria.

It uses the BasicMCPClient to maintain a persistent connection. This allows for asynchronous tool execution during your indexing process.

The server only interacts with public economic metadata. It never accesses your private knowledge base, and your vector store remains isolated from the FRED API calls.

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