How to Use the US Wrongful Termination Calculator MCP in Pydantic AI

Type-safe legal estimation using Pydantic AI and this MCP.

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Connect US Wrongful Termination Calculator MCP to Pydantic AI

Create your Vinkius account to connect US Wrongful Termination 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 Wrongful Termination Calculator with Pydantic AI

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Fetch State Rules Safely

You need accurate jurisdictional data before drafting a demand letter. The `lookup_state_punitive_standard` tool pulls the exact punitive damage rules for a specific state. It gives you the statutory reality of the venue. Pydantic AI validates every piece of this response at runtime. If the API returns a string instead of an expected integer for a damage cap, the agent fails loudly. You never get silent corruption in your legal research.

Crunch Settlement Numbers with MCP

Litigation boils down to dollars and cents. Executing `calculate_damages_estimate` computes the potential damages for a wrongful termination claim based on wage inputs. You get a concrete financial baseline. The unified MCPToolset handles the connection perfectly. You pass the HTTP URL directly during setup. The framework ensures the math matches your strict Pydantic models before passing it to the user.

Grade Case Viability

Taking a bad case costs money. We use `assess_claim_risk_profile` to determine the qualitative legal risk level of a claim. It highlights the weaknesses in the employee narrative. Being model-agnostic means you pick the best brain for the job. You run this analysis through a local model for privacy or a hosted model for speed. The validation guarantees the output format remains identical either way.

Setup guide

Set up US Wrongful Termination 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-wrongful-termination-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 Wrongful Termination Calculator tools.",
)

result = await agent.run("List recent US Wrongful Termination 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 Wrongful Termination 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 Wrongful Termination Calculator MCP in Pydantic AI

Install the slim package with the MCP extra. Create an MCPToolset with your connection string. Pass it to the toolsets list in your Agent setup.

No. The framework validates every response against your predefined models. Bad data triggers an immediate validation error.

Yes. The framework is completely model-agnostic. You swap between hosted and local providers without changing your tool code.

No. That class is deprecated. Use the unified MCPToolset approach for cleaner code.

Claimant ages and tenure lengths are processed here. We enforce strict ephemeral memory policies and never log personal identifiers to persistent storage.

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