How to Use the Federal Register MCP in Pydantic AI

Strictly type-safe Federal Register data access for Pydantic AI agents, ensuring zero runtime data corruption.

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Connect Federal Register MCP to Pydantic AI

Create your Vinkius account to connect Federal Register 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 Federal Register with Pydantic AI

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Type-validated regulatory data

Pydantic AI ensures that every piece of data returned by `get_document` matches your expected schema. If the API structure shifts, your agent catches the validation error immediately. This prevents your system from hallucinating fields or crashing on malformed responses. It’s the safest way to handle government data.

Precise document searching

Execute `search_documents` and have the results automatically validated against your Pydantic models. You get clean, typed objects every time. Your agent can then run logic on these results with total confidence in the data integrity. It removes the need for manual parsing or custom cleanup scripts.

Inspection notice monitoring

Use `search_public_inspection` to pull upcoming notices into your Pydantic-based workflow. The server returns the data, and your agent validates it against your type definitions. It allows you to build a robust pipeline that only acts on verified information. You’ll know exactly what’s in the data before your agent makes a decision.

Setup guide

Set up Federal Register 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": {
        "federal-register-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Federal Register tools.",
)

result = await agent.run("List recent Federal Register transactions")
print(result.output)

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Common questions about Federal Register MCP in Pydantic AI

When the MCP Server returns data, Pydantic AI checks it against your defined models. If the schema doesn't match, the agent throws a validation error.

Yes. You pass the server as a toolset to your agent, and all incoming document data undergoes strict type validation.

The server provides the data, and your Pydantic models verify the structure of the search results. Any unexpected format is caught at the boundary.

Vinkius provides a secure, ephemeral sandbox. Your agent connects via a single token, ensuring that communication remains isolated.

The server acts only as a bridge to public records. No data, including your Pydantic model schemas or agent logs, is persisted on the host.

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