How to Use the Fiscal Data (U.S. Treasury) MCP in Pydantic AI

Q: How do I configure this MCP Server for Fiscal Data (U.S. Treasury) in Pydantic AI?

Install the slim MCP package and pass the server URL to MCPToolset. It handles the transport layer so your agent can focus on executing the logic.

Strictly type-checked Fiscal Data (U.S. Treasury) access for your Pydantic AI agent fleet.

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Connect Fiscal Data (U.S. Treasury) MCP to Pydantic AI

Create your Vinkius account to connect Fiscal Data (U.S. Treasury) 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 Fiscal Data (U.S. Treasury) with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Validated Treasury Statement Retrieval

Your agent fetches `get_mts_table_1` and `get_mts_table_9` data, which is immediately checked against your Pydantic schemas. If the Treasury changes a field, your agent stops and alerts you. This prevents bad data from poisoning your financial models. You only process numbers that match your expected type definitions.

Type-Safe Exchange Rate Processing

Every rate pulled by `get_rates_of_exchange` passes through a strict Pydantic model. You get runtime validation for every currency pair fetched. This eliminates the risk of silent corruption in your exchange rate calculations. Your agent handles data as a structured object, not just a blob of text.

Querying Fiscal Datasets with Precision

The `query_dataset` tool works with your custom models to enforce data integrity across all 170+ Treasury datasets. You define the structure, and the agent enforces it. This keeps your agent output consistent. You avoid hallucinated fields by requiring the agent to map the Treasury response to your defined Pydantic class.

Setup guide

Set up Fiscal Data (U.S. Treasury) 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": {
        "fiscal-data-us-treasury-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Fiscal Data (U.S. Treasury) tools.",
)

result = await agent.run("List recent Fiscal Data (U.S. Treasury) transactions")
print(result.output)

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Common questions about Fiscal Data (U.S. Treasury) MCP in Pydantic AI

Each tool output is parsed into a Pydantic model. If the Treasury API returns a response that doesn't fit your schema, the agent throws a validation error immediately.

Yes, it is highly reliable because it enforces a strict contract between the API and your agent. This prevents common runtime errors caused by unexpected nulls or type mismatches.

Install the slim MCP package and pass the server URL to `MCPToolset`. It handles the transport layer so your agent can focus on executing the logic.

Yes, the framework is model-agnostic. Whether you run a local model or a cloud-hosted LLM, the Pydantic validation ensures the Treasury data is handled correctly.

Your agent only interacts with the public Treasury API endpoints. All data objects are validated in memory and never persisted in an insecure format by the MCP Server itself.

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