How to Use the FRED Series — U.S. Economic Time Series MCP in LlamaIndex

Q: How do I load the FRED Series — U.S. Economic Time Series MCP Server into LlamaIndex?

Run pip install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it with McpToolSpec(client=mcpclient), and pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex RAG applications query the FRED Series — U.S. Economic Time Series by keyword?

They can. The agent triggers the searchseries tool with a keyword phrase. It reads the returned titles and popularity metrics to select the right dataset for indexing.

Q: Does the FRED Series — U.S. Economic Time Series pull live updates for LlamaIndex?

The getseriesupdates tool fetches recently updated series. Your LlamaIndex agent can run this periodically to refresh its vector store with the newest macroeconomic releases.

Index over 816,000 U.S. economic time series directly into your LlamaIndex RAG applications using this MCP connection.

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Connect FRED Series — U.S. Economic Time Series MCP to LlamaIndex

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Setup FRED Series — U.S. Economic Time Series with LlamaIndex

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Index Economic Observations

The `get_observations` tool extracts actual data values from FRED time series and applies unit transformations before returning the payload. Your LlamaIndex application takes these raw numbers, chunks them, and embeds them into a queryable vector store. This turns static RAG into a live economic oracle. When a user asks about inflation trends, the agent pulls the latest CPI numbers, indexes them on the fly, and grounds its answer in actual government data.

LlamaIndex MCP Server for Metadata

The `search_series` tool searches the 816,000+ economic indicators by keyword to find the right series IDs, while `get_series` pulls the exact metadata. Your RAG pipeline uses these tools to build a semantic map of available economic metrics. Instead of hallucinating ticker symbols or economic IDs, your agent queries the MCP Server to find "unemployment rate" or "M2SL". It then stores that metadata context alongside the actual time series data.

Ground RAG in Historical Revisions

The `get_vintage_dates` tool pulls historical revision dates, which is critical for ALFRED-style vintage analysis. Your LlamaIndex agent uses this to understand exactly when GDP or inflation numbers changed after their initial release. Pair this with `get_series_updates` to keep your vector store current. The agent spots new data releases for macro or regional series and automatically updates the index, preventing your application from serving stale economic reports.

Setup guide

Set up FRED Series — U.S. Economic Time Series 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 Series — U.S. Economic Time Series 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 Series — U.S. Economic Time Series tools.",
)
response = await agent.run("List recent FRED Series — U.S. Economic Time Series data")

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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 Series — U.S. Economic Time Series MCP in LlamaIndex

Run `pip install llama-index-tools-mcp`. Set up a `BasicMCPClient`, wrap it with `McpToolSpec(client=mcp_client)`, and pass the async tool list to your `FunctionAgent`.

They can. The agent triggers the `search_series` tool with a keyword phrase. It reads the returned titles and popularity metrics to select the right dataset for indexing.

The `get_series_updates` tool fetches recently updated series. Your LlamaIndex agent can run this periodically to refresh its vector store with the newest macroeconomic releases.

You pass parameters like frequency aggregation or percent change directly to the `get_observations` tool. The server returns the transformed data, ready for your embedding model.

You are pulling public indicators like the Federal Funds Rate and CPI. The MCP server executes these pulls inside a zero-trust environment. No query history or retrieved economic data is stored on Vinkius infrastructure.

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