How to Use the FRED GeoFRED — Regional Economic Data MCP in LlamaIndex

Q: Can LlamaIndex agents automatically resolve series IDs using FRED GeoFRED — Regional Economic Data?

Yes. The agent uses getseriesgroup to discover the correct series group ID before calling getregionaldata, handling the entire lookup process autonomously.

Index regional Fed economic data directly into LlamaIndex vector stores for grounded, hallucination-free RAG.

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Connect FRED GeoFRED — Regional Economic Data MCP to LlamaIndex

Create your Vinkius account to connect FRED GeoFRED — Regional Economic Data 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 GeoFRED — Regional Economic Data with LlamaIndex

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Map regional economic metrics using this MCP Server

`get_regional_data` acts as the primary data fetcher for harvesting local economic metrics like county GDP or MSA median income. Your agent queries this endpoint to pull current, localized metrics from the Fed's databases. LlamaIndex takes these raw numbers and indexes them into your document store. This ensures your synthesis engine is always grounded in verified government statistics rather than outdated training data.

Resolve FRED indicators to regional series groups

`get_series_group` identifies the correct regional series group ID for any standard FRED indicator. This step is necessary because regional data is organized differently than national averages. When building a LlamaIndex query pipeline, the agent uses this tool to map the user's intent to the correct geographic dataset. By checking the available region types and seasonality upfront, the pipeline avoids broken queries and saves execution time.

Extract spatial boundaries for regional analysis

`get_geo_shapes` pulls geographic boundary coordinates for counties, MSAs, and Federal Reserve districts. This tool lets your indexing pipeline pair statistical data with physical geographic coordinates. In LlamaIndex, store these coordinates alongside the regional economic metrics in a unified vector index. This lets your RAG application answer complex spatial queries, such as identifying economic trends across contiguous counties.

Setup guide

Set up FRED GeoFRED — Regional Economic Data 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 GeoFRED — Regional Economic Data 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 GeoFRED — Regional Economic Data tools.",
)
response = await agent.run("List recent FRED GeoFRED — Regional Economic Data data")

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Common questions about FRED GeoFRED — Regional Economic Data MCP in LlamaIndex

Use the MCP tool package to load the tools. Once fetched, write the outputs from `get_regional_data` directly into your Document objects and index them into a vector store.

Yes. The agent uses `get_series_group` to discover the correct series group ID before calling `get_regional_data`, handling the entire lookup process autonomously.

The MCP server itself provides structured data, which LlamaIndex then indexes into vector stores. This enables semantic queries over regional economic data, like finding metro areas with similar unemployment trends.

Initialize the MCP client with your Vinkius endpoint, convert it with `McpToolSpec`, and pass the tools to your `FunctionAgent`. This exposes the geographic endpoints to your agent.

This integration only requests public regional economic metrics and spatial coordinates. Vinkius secures your connection with a single endpoint token and runs the server in a zero-trust sandbox, preventing any data leaks.

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