How to Use the FRED Full Access — U.S. Economic Intelligence MCP in LlamaIndex

Q: How do I connect FRED Full Access — U.S. Economic Intelligence to LlamaIndex?

Install llama-index-tools-mcp. Initialize BasicMCPClient with the endpoint, pass it to McpToolSpec, and convert the tools for your FunctionAgent.

Q: How does the agent find the right economic indicator?

It calls searchseries with keyword arguments. The agent reads the returned list, picks the series with the highest popularity score, and proceeds to fetch the observations.

Index raw Federal Reserve data into your LlamaIndex vector stores to build grounded, hallucination-free economic RAG apps.

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Connect FRED Full Access — U.S. Economic Intelligence MCP to LlamaIndex

Create your Vinkius account to connect FRED Full Access — U.S. Economic Intelligence 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 Full Access — U.S. Economic Intelligence with LlamaIndex

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Time Series Ingestion

The `get_observations` tool pulls actual Federal Reserve time series data directly into your LlamaIndex knowledge base. Your application ingests decades of historical GDP or inflation numbers and indexes them alongside your internal research documents. When a user queries your agent about historical rate hikes, semantic search retrieves the exact data points. The agent synthesizes the raw numbers with your stored reports, delivering answers grounded in actual economic history.

Documenting Data with this MCP Server

The `get_series` tool fetches metadata for any of the 816,000+ available economic indicators. You pass this directly to `McpToolSpec`, allowing your FunctionAgent to read the frequency, units, and source notes before querying the numbers. This metadata becomes part of your unified index. If a user asks how a specific employment metric is calculated, the agent pulls the exact source documentation rather than guessing.

Spatial RAG Applications

The `get_regional_data` tool extracts cross-sectional regional economic metrics for your spatial RAG applications. Your agent uses `get_series_group` to find the correct ID, then pulls the state or county-level data. You index these regional figures to answer complex geographic queries. The user asks which census regions saw the highest wage growth, and the agent cross-references the indexed GeoFRED data to generate the response.

Setup guide

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

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Common questions about FRED Full Access — U.S. Economic Intelligence MCP in LlamaIndex

Install `llama-index-tools-mcp`. Initialize `BasicMCPClient` with the endpoint, pass it to `McpToolSpec`, and convert the tools for your `FunctionAgent`.

You store the time series and metadata in your vector store. The agent queries this indexed data instead of hitting the live API for every question.

It calls `search_series` with keyword arguments. The agent reads the returned list, picks the series with the highest popularity score, and proceeds to fetch the observations.

You eliminate hallucinations in economic analysis. The agent cites real Federal Reserve data points stored in your index rather than generating plausible but fake numbers.

The MCP server only handles inbound requests for public economic indicators, release schedules, and source metadata. Your vector store contents and LlamaIndex configurations never leave your private infrastructure.

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