What is the difference between tags and categories?

Categories are a strict hierarchy (one parent, many children). Tags are flat labels — each series can have many tags across dimensions: geography (usa), frequency (monthly), topic (gdp), source (bls). Combine tags for powerful cross-dimensional filtering.

FRED Tags & Sources — Data Discovery MCP Server for Pydantic AI 3 tools — connect in under 2 minutes

Name: FRED Tags & Sources — Data Discovery
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 3 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect FRED Tags & Sources — Data Discovery through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to FRED Tags & Sources — Data Discovery "
            "(3 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in FRED Tags & Sources — Data Discovery?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every FRED Tags & Sources — Data Discovery tool response against typed schemas, catching data inconsistencies at build time. Connect 3 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the FRED Tags & Sources — Data Discovery MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the FRED Tags & Sources — Data Discovery MCP Server

Pydantic AI provides unique advantages when paired with FRED Tags & Sources — Data Discovery through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your FRED Tags & Sources — Data Discovery integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your FRED Tags & Sources — Data Discovery connection logic from agent behavior for testable, maintainable code

FRED Tags & Sources — Data Discovery + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the FRED Tags & Sources — Data Discovery MCP Server delivers measurable value.

Type-safe data pipelines: query FRED Tags & Sources — Data Discovery with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple FRED Tags & Sources — Data Discovery tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query FRED Tags & Sources — Data Discovery and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock FRED Tags & Sources — Data Discovery responses and write comprehensive agent tests

FRED Tags & Sources — Data Discovery MCP Tools for Pydantic AI (3)

These 3 tools become available when you connect FRED Tags & Sources — Data Discovery to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting FRED Tags & Sources — Data Discovery to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

FRED Tags & Sources — Data Discovery + Pydantic AI FAQ

Common questions about integrating FRED Tags & Sources — Data Discovery MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your FRED Tags & Sources — Data Discovery MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.