How to Use the Settlement Value Estimator MCP in Pydantic AI

Build strict legal finance tools with Pydantic AI and our managed MCP.

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MCP Servers — Included with Plan

Connect Settlement Value Estimator MCP to Pydantic AI

Create your Vinkius account to connect Settlement Value Estimator 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 Settlement Value Estimator with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Type-safe financial math.

Pydantic AI guarantees your agent gets exact numbers back from this MCP. You pass in case probabilities and potential outcomes. Calling `calculate_emv` returns a validated expected monetary value object. If the math breaks or data types mismatch, the framework throws a loud validation error instead of silently hallucinating a bad settlement number.

Strict Pydantic AI boundaries.

Legal negotiations require hard limits. Your agent needs to know exactly where to start and stop bargaining. Running `derive_negotiation_surfaces` plots the best and worst alternative limits. You get validated BATNA and WATNA fields mapped directly to your Python models.

Stress testing outcomes.

Risk tolerance changes case values fast. You need to see how small shifts in win probability impact the final dollar amount. Triggering `perform_risk_sensitivity_analysis` runs the Monte Carlo simulation. The agent receives a strict array of risk-adjusted settlement values ready for downstream processing.

Setup guide

Set up Settlement Value Estimator 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": {
        "settlement-value-estimator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Settlement Value Estimator transactions")
print(result.output)

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Common questions about Settlement Value Estimator MCP in Pydantic AI

Install the mcp slim package via pip. Pass the Vinkius URL into an MCPToolset and hand it to your Agent toolsets list.

Yes. Every expected value or limit returned by this MCP gets checked against your Python schemas at runtime.

Any model the framework supports will work. The agent reads the tool definitions from our catalog and executes the math regardless of the underlying LLM.

No. Vinkius manages over 4,000 tools including this one. You just connect your client once and start calling functions.

The integration only processes the financial inputs and probability percentages you send it. Vinkius drops these numbers from memory immediately after returning the calculation.

Use it with your favorite AI tools

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