How to Use the FRED Categories — Economic Data Taxonomy MCP in LlamaIndex

Q: How do I index the FRED Categories — Economic Data Taxonomy using LlamaIndex?

Use the MCP tool spec with the BasicMCPClient to load the taxonomy tools. Your LlamaIndex agent executes getcategorychildren and writes the returned metadata directly into your document index.

Q: Can LlamaIndex agents search FRED Categories — Economic Data Taxonomy semantically?

Yes. The agent uses getcategorytags to fetch tags, indexes those tags into a vector store, and maps semantic search queries to specific FRED category IDs.

Q: How does FRED Categories — Economic Data Taxonomy prevent hallucinated data in LlamaIndex?

The MCP Server forces the agent to call getcategory to confirm a category exists before querying its series. Grounding your queries in live metadata prevents the agent from inventing non-existent economic metrics.

Q: What is the installation process for LlamaIndex?

Run pip install llama-index-tools-mcp to get the core integration. Initialize the BasicMCPClient with the server URL, convert the tools using totoollistasync(), and pass them to your agent.

Index FRED economic category metadata into your LlamaIndex vector store for grounded macroeconomic RAG.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect FRED Categories — Economic Data Taxonomy MCP to LlamaIndex

Create your Vinkius account to connect FRED Categories — Economic Data Taxonomy 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.

GDPR Free for Subscribers

Setup FRED Categories — Economic Data Taxonomy with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Feed live FRED taxonomy data into LlamaIndex RAG

The `get_category_series` tool fetches active economic series from FRED categories to ground your RAG applications in real-time data. Your LlamaIndex agent pulls these series, indexes them, and queries the vector store to answer complex macro questions. This process eliminates hallucinations because the agent references actual taxonomy structures and series IDs. You get a searchable knowledge base grounded in official economic classifications.

Navigate categories using this MCP Server

The `get_category_children` tool maps parent-child relationships from root category 0 down to niche regional data. Your LlamaIndex agent uses this tool to build a hierarchical index of the economic taxonomy. This structure helps the agent locate relevant series when a user asks high-level thematic questions. Instead of guessing, the agent queries the live taxonomy to find the exact category ID.

Filter indicators with taxonomic tags

The `get_category_tags` and `get_category` tools provide the raw metadata required to filter series before indexing. Your LlamaIndex agent checks these tags to group series by frequency, geography, or economic sector. This filtering ensures your vector database only indexes relevant, high-frequency series. You save vector storage space and improve retrieval precision by pre-filtering data at the category level.

Setup guide

Set up FRED Categories — Economic Data Taxonomy 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 Categories — Economic Data Taxonomy 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 Categories — Economic Data Taxonomy tools.",
)
response = await agent.run("List recent FRED Categories — Economic Data Taxonomy 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect FRED Categories — Economic Data Taxonomy now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about FRED Categories — Economic Data Taxonomy MCP in LlamaIndex

Use the MCP tool spec with the `BasicMCPClient` to load the taxonomy tools. Your LlamaIndex agent executes `get_category_children` and writes the returned metadata directly into your document index.

Yes. The agent uses `get_category_tags` to fetch tags, indexes those tags into a vector store, and maps semantic search queries to specific FRED category IDs.

The MCP Server forces the agent to call `get_category` to confirm a category exists before querying its series. Grounding your queries in live metadata prevents the agent from inventing non-existent economic metrics.

Run `pip install llama-index-tools-mcp` to get the core integration. Initialize the `BasicMCPClient` with the server URL, convert the tools using `to_tool_list_async()`, and pass them to your agent.

This MCP Server only processes public FRED taxonomy codes, category names, and tag strings. All execution occurs in a zero-trust, ephemeral V8 isolate that destroys its own memory footprint immediately after the run.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript