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

Ensure data integrity in your Pydantic AI workflows by linking to the FRED Categories — Economic Data Taxonomy MCP Server.

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Connect FRED Categories — Economic Data Taxonomy MCP to Pydantic AI

Create your Vinkius account to connect FRED Categories — Economic Data Taxonomy 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 Categories — Economic Data Taxonomy with Pydantic AI

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Perplexity

Validate taxonomy data in Pydantic AI

Every response from `get_category` is checked against your Pydantic models. If the server returns unexpected fields, your agent catches the error immediately. This prevents your agent from building models on corrupted data. It enforces strict structural integrity across your entire economic analysis pipeline.

Retrieve series with type safety

Use `get_category_series` to pull data into your typed agent functions. The tool output is validated at runtime to match your expected schema. This eliminates silent failures when the underlying data format changes. You get runtime guarantees that your agent's inputs are always valid.

Query tags with strict models

Your agent uses `get_category_tags` to discover valid dimensions. Each tag is mapped to a Pydantic model to ensure consistency. This makes your agent's filtering logic predictable. By validating the tags, you guarantee that your agent only uses supported filters for its queries.

Setup guide

Set up FRED Categories — Economic Data Taxonomy 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-categories-economic-data-taxonomy-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about FRED Categories — Economic Data Taxonomy MCP in Pydantic AI

The MCP toolset automatically maps the server output to your Pydantic models. If the data doesn't match, the agent throws a validation error.

Yes, you can define a Pydantic model for your cache and store the results. This allows your agent to work offline or reduce API usage.

It supports SSE and HTTP. You just need to configure the MCPToolset with the correct transport URL.

Your Pydantic model handles the empty state gracefully. You can define optional fields to prevent the agent from failing on missing data.

The server only provides public economic indicators. Your local environment remains isolated, and no data leaves your control during validation.

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