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

Ensure type-safe regional economic analysis with Pydantic AI and the FRED GeoFRED MCP Server.

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Connect FRED GeoFRED — Regional Economic Data MCP to Pydantic AI

Create your Vinkius account to connect FRED GeoFRED — Regional Economic Data to Pydantic AI 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 Pydantic AI

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Perplexity

Enforce data schema with Pydantic AI

Every response from `get_regional_data` is validated against your Pydantic models the moment it arrives. If the economic data looks wrong or missing, the agent fails immediately rather than processing bad numbers. This protects your analytical pipeline from silent errors. You get the peace of mind that your agent is working with exactly the data structure you defined.

Retrieve spatial boundaries with strict typing

When you call `get_geo_shapes`, the tool returns data that your agent validates on the fly. This ensures your maps are always rendered using reliable coordinate data. You avoid the common issue of hallucinated fields or malformed GeoJSON. The Pydantic AI framework keeps your agent's output consistent every single time.

Check series metadata before execution

Use `get_series_group` to fetch the metadata required for your queries. Pydantic AI validates these group configurations to ensure your agent understands the unit and frequency of the series it's about to pull. This makes your code more predictable. Your agent won't attempt to process data that doesn't fit your established economic models.

Setup guide

Set up FRED GeoFRED — Regional Economic Data MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "fred-geofred-regional-economic-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to FRED GeoFRED — Regional Economic Data tools.",
)

result = await agent.run("List recent FRED GeoFRED — Regional Economic Data transactions")
print(result.output)

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Common questions about FRED GeoFRED — Regional Economic Data MCP in Pydantic AI

It provides runtime validation that prevents data corruption. When you connect FRED GeoFRED — Regional Economic Data, the agent rejects any malformed output immediately.

Use the MCPToolset class with your HTTP endpoint. Once initialized, simply pass it into the toolsets list of your Agent instance.

Yes, it supports both Streamable HTTP and SSE transports. This allows your agent to receive data in chunks for faster processing of large regional datasets.

Your Pydantic models will catch the discrepancy immediately. The agent will raise a validation error instead of hallucinating values or failing silently during analysis.

We only process public economic series IDs and regional data. No private files or user data are ever touched; the security is enforced by your strictly typed schema definitions.

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