FRED Tags & Sources — Data Discovery MCP Server for LlamaIndex 3 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FRED Tags & Sources — Data Discovery as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to FRED Tags & Sources — Data Discovery. "
"You have 3 tools available."
),
)
response = await agent.run(
"What tools are available in FRED Tags & Sources — Data Discovery?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About FRED Tags & Sources — Data Discovery MCP Server
The discovery layer for FRED. Tags & Sources helps your AI agent find exactly the right series by filtering through FRED's comprehensive tagging system.
LlamaIndex agents combine FRED Tags & Sources — Data Discovery tool responses with indexed documents for comprehensive, grounded answers. Connect 3 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Search Tags — Browse geographic (usa, europe), topic (gdp, inflation), source (bls, bea), and frequency (monthly, quarterly) tags
- Tag Combinations — Find series matching ALL specified tags (e.g., usa + gdp + quarterly) while excluding others
- Data Sources — List all 107 organizations contributing data: BLS, BEA, Federal Reserve Board, Census Bureau, Treasury, IMF, and more
The FRED Tags & Sources — Data Discovery MCP Server exposes 3 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect FRED Tags & Sources — Data Discovery to LlamaIndex via MCP
Follow these steps to integrate the FRED Tags & Sources — Data Discovery MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 3 tools from FRED Tags & Sources — Data Discovery
Why Use LlamaIndex with the FRED Tags & Sources — Data Discovery MCP Server
LlamaIndex provides unique advantages when paired with FRED Tags & Sources — Data Discovery through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine FRED Tags & Sources — Data Discovery tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain FRED Tags & Sources — Data Discovery tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query FRED Tags & Sources — Data Discovery, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what FRED Tags & Sources — Data Discovery tools were called, what data was returned, and how it influenced the final answer
FRED Tags & Sources — Data Discovery + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the FRED Tags & Sources — Data Discovery MCP Server delivers measurable value.
Hybrid search: combine FRED Tags & Sources — Data Discovery real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query FRED Tags & Sources — Data Discovery to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying FRED Tags & Sources — Data Discovery for fresh data
Analytical workflows: chain FRED Tags & Sources — Data Discovery queries with LlamaIndex's data connectors to build multi-source analytical reports
FRED Tags & Sources — Data Discovery MCP Tools for LlamaIndex (3)
These 3 tools become available when you connect FRED Tags & Sources — Data Discovery to LlamaIndex via MCP:
get_series_by_tags
Powerful for discovering related series. Example: tag_names="usa;gdp" returns all US GDP series. Combine with exclude_tag_names to refine. Get FRED series matching specific tags
list_sources
List all FRED data sources
search_tags
Search by text or get all tags. Tags include geographic (usa, europe), topic (gdp, inflation), source (bls, bea), and frequency (monthly, quarterly) labels. Search or browse FRED tags
Example Prompts for FRED Tags & Sources — Data Discovery in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with FRED Tags & Sources — Data Discovery immediately.
"Find all monthly U.S. GDP-related series"
"List all data sources that contribute to FRED"
"What tags are most popular on FRED?"
Troubleshooting FRED Tags & Sources — Data Discovery MCP Server with LlamaIndex
Common issues when connecting FRED Tags & Sources — Data Discovery to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFRED Tags & Sources — Data Discovery + LlamaIndex FAQ
Common questions about integrating FRED Tags & Sources — Data Discovery MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect FRED Tags & Sources — Data Discovery with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect FRED Tags & Sources — Data Discovery to LlamaIndex
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.