How to Use the US WARN Act Compliance Calculator MCP in Pydantic AI

Calculate exact employment liabilities with strict type safety in Pydantic AI.

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Connect US WARN Act Compliance Calculator MCP to Pydantic AI

Create your Vinkius account to connect US WARN Act Compliance Calculator to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup US WARN Act Compliance Calculator with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Type-Safe MCP Compliance Checks

An agent executes `check_warn_compliance` to verify if your planned headcount reductions cross the legal threshold. Federal notice requirements demand absolute accuracy. Here's the thing. Silent errors in legal math will bankrupt you. Pydantic validates every response against strict models so you never get hallucinated compliance approvals.

Back Pay Liability Calculations

Your agent triggers `calculate_employee_backpay_liability` to compute the exact dollar amount owed to the affected staff. Terminating workers without proper notice triggers massive wage obligations. The calculation processes daily pay rates and lost benefits. Loud failures happen immediately if the API returns a string instead of a float for the dollar amount.

Municipal Penalty Projections

An agent runs `calculate_civil_penalty_exposure` to tally the daily fines for ignoring municipal notice requirements. Local governments enforce strict notification rules. Bottom line: you need exact figures. Model-agnostic setups ensure you get the same precise penalty calculation whether you run Claude or a local model.

Setup guide

Set up US WARN Act Compliance Calculator 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-warn-act-compliance-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to US WARN Act Compliance Calculator tools.",
)

result = await agent.run("List recent US WARN Act Compliance Calculator transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by US WARN Act Calculator. 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.

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Common questions about US WARN Act Compliance Calculator MCP in Pydantic AI

Install the slim package with the MCP extra. Create a toolset using your Vinkius HTTP URL and attach it to your Agent.

Standard scripts lack runtime validation. This framework ensures every liability calculation strictly matches your expected data structures.

The framework supports both Streamable HTTP and SSE transports. You connect to the external Vinkius endpoint using either method.

The agent throws a validation error immediately. You get loud failures instead of silently corrupted compliance reports.

The calculations ingest hourly wage rates and benefit costs. Vinkius isolates these financial inputs in stateless memory blocks that wipe clean the millisecond the API response completes.

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