How to Use the FRED Full Access — U.S. Economic Intelligence MCP in Pydantic AI

Q: How do I ensure type safety with FRED Full Access — U.S. Economic Intelligence in Pydantic AI?

Use the MCPToolset class from the pydantic-ai-slim package. It automatically validates every tool output against your defined models.

Strictly type-safe economic analysis using FRED Full Access — U.S. Economic Intelligence and Pydantic AI.

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Connect FRED Full Access — U.S. Economic Intelligence MCP to Pydantic AI

Create your Vinkius account to connect FRED Full Access — U.S. Economic Intelligence 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 Full Access — U.S. Economic Intelligence with Pydantic AI

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Perplexity

Enforce schema integrity in Pydantic AI

Every response from `get_series` is validated against your Pydantic models at runtime. If the API returns malformed data, your agent stops immediately. This prevents silent errors in your financial modeling. You get clean, typed data that your agent can trust for every calculation.

Query categories with Pydantic AI

Navigate complex data hierarchies using `get_category_children`. Your agent explores the tree structure to find the exact branch of economic data it needs. It ensures you don't waste time on irrelevant series. The agent drills down until it finds the correct, verified data set for your specific query.

Aggregate data with Pydantic AI

Use `get_observations` with unit transformations to get pre-calculated values. Your agent requests specifically formatted data, reducing the need for local post-processing. This keeps your agent's code lean. It delegates the heavy lifting to the server while you maintain strict control over the incoming data types.

Setup guide

Set up FRED Full Access — U.S. Economic Intelligence 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-full-access-us-economic-intelligence-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to FRED Full Access — U.S. Economic Intelligence tools.",
)

result = await agent.run("List recent FRED Full Access — U.S. Economic Intelligence transactions")
print(result.output)

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Common questions about FRED Full Access — U.S. Economic Intelligence MCP in Pydantic AI

Use the `MCPToolset` class from the `pydantic-ai-slim` package. It automatically validates every tool output against your defined models.

Yes, the server supports both Streamable HTTP and SSE transports. This ensures a stable connection for your type-sensitive agent workflows.

Yes, the framework is model-agnostic. Whether you use a local model or a cloud-based one, the validation remains consistent across all agents.

The tool will return a descriptive error. Because you are using Pydantic AI, this failure is caught and handled immediately by your agent's error logic.

The server operates in a sandbox and only processes public time series values. No sensitive information is involved, keeping your analysis environment secure.

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