How to Use the U.S. Treasury Debt — National Debt & Interest Rates MCP in Pydantic AI

Q: Can I track past debt figures using U.S. Treasury Debt — National Debt & Interest Rates with Pydantic AI?

Yes, you use getdebthistory for specific date ranges. The data comes through a validated stream, meaning your agent can confidently compare historical numbers without worrying about schema drift.

Q: Does U.S. Treasury Debt — National Debt & Interest Rates include auction data for Pydantic AI?

It does, via the gettreasuryauctions tool. The results—including the bid-to-cover ratio—are validated against your schema before hitting your application logic.

Validate U.S. Treasury Debt & Rates Output with Pydantic AI for Correctness.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect U.S. Treasury Debt — National Debt & Interest Rates MCP to Pydantic AI

Create your Vinkius account to connect U.S. Treasury Debt — National Debt & Interest Rates 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.

GDPR Free for Subscribers

Setup U.S. Treasury Debt — National Debt & Interest Rates with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Verify National Debt Status using MCP Server

The `get_national_debt` tool provides the national debt total, updated daily by the Treasury. It gives you the figure to the penny, breaking it down into 'Debt Held by the Public' and 'Intragovernmental Holdings'. Because correctness is everything in your pipelines, this data feeds directly into Pydantic models. If the API structure changes unexpectedly, the agent fails loudly with a validation error—no silent corruption.

Get Interest Rates via MCP Server

Use `get_avg_interest_rates` to fetch average interest rates on U.S. Treasury securities, reflecting the government's borrowing cost. This rate updates every month. This toolset ensures that when your agent processes financial data, the input for key economic variables—like these interest rates—is always structured and validated against a Pydantic schema.

Get Public Debt Structure via MCP Server

The `get_public_debt_breakdown` tool separates 'Debt Held by the Public' from 'Intragovernmental Holdings'. This gives your agent the necessary structural detail. This reliable, type-safe data allows you to build complex financial logic. You don't have to worry about mismatched fields or unexpected nulls because Pydantic validates every single field upon receipt.

Setup guide

Set up U.S. Treasury Debt — National Debt & Interest Rates 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": {
        "us-treasury-debt-national-debt-interest-rates-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to U.S. Treasury Debt — National Debt & Interest Rates tools.",
)

result = await agent.run("List recent U.S. Treasury Debt — National Debt & Interest Rates transactions")
print(result.output)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by U.S. Department of the Treasury. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect U.S. Treasury Debt — National Debt & Interest Rates now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about U.S. Treasury Debt — National Debt & Interest Rates MCP in Pydantic AI

It provides structured financial data that your agent can map directly to Pydantic models for runtime validation. This prevents the entire pipeline from breaking due to bad or unexpected API responses.

Yes, you use `get_debt_history` for specific date ranges. The data comes through a validated stream, meaning your agent can confidently compare historical numbers without worrying about schema drift.

It does, via the `get_treasury_auctions` tool. The results—including the bid-to-cover ratio—are validated against your schema before hitting your application logic.

The server touches various financial metrics, including 'Total Public Debt Outstanding', average interest rates, and historical debt figures.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python