How to Use the Settlement Value Estimator MCP in CrewAI

Use the Settlement Value Estimator MCP to give your CrewAI agents the math they need to negotiate legal outcomes with objective precision.

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Connect Settlement Value Estimator MCP to CrewAI

Create your Vinkius account to connect Settlement Value Estimator to CrewAI — 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 CrewAI

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Quantify litigation risk with CrewAI

Your agents can now run a cold, hard `calculate_emv` to determine the expected value of any legal claim. It strips away the emotional bias that ruins settlement talks. By feeding probability-weighted outcomes directly into the crew's memory, you ensure every decision rests on mathematical reality rather than gut feeling. It is the fastest way to get your agents on the same page.

Map negotiation zones in CrewAI

Stop guessing where the deal lands and let `derive_negotiation_surfaces` plot the ZOPA, BATNA, and WATNA boundaries for you. Your agents identify the exact range where a deal makes financial sense. This MCP provides the structure required for complex multi-agent negotiations. It defines the floor and the ceiling, so your crew knows exactly when to walk away or push for more.

Stress test outcomes via this MCP

Run `perform_risk_sensitivity_analysis` to see how your legal position shifts when win probabilities fluctuate. It exposes your vulnerability to catastrophic jury awards before you commit to a trial. This tool allows your agents to simulate extreme scenarios and adjust their risk tolerance accordingly. It turns a static legal strategy into a dynamic, defensive asset for your firm.

Setup guide

Set up Settlement Value Estimator MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Settlement Value Estimator tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Settlement Value Estimator Analyst",
    goal="Access and analyze Settlement Value Estimator data via MCP.",
    backstory="Expert analyst with direct Settlement Value Estimator access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Settlement Value Estimator transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Settlement Value Estimator Analyst",
        goal="Access and analyze Settlement Value Estimator data via MCP.",
        backstory="Expert analyst with direct Settlement Value Estimator access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Settlement Value Estimator transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 Settlement Value Estimator. 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 Settlement Value Estimator MCP in CrewAI

It provides a rigid framework for calculating expected value. Your agents use this data to compare settlement offers against the statistical cost of a trial.

The output is purely mathematical, based on the variables you provide. It removes human error and keeps your agents focused on the numbers that matter.

Yes, you simply add the endpoint to your agent configuration. It connects directly to your existing agents so they can call tools without extra plumbing.

This MCP handles only the raw inputs you provide for calculation. None of your litigation strategy or private case files are stored or shared by the tool.

The tool relies on the quality of the probabilities you input. It is a calculator, not a lawyer, so ensure your data inputs are accurate to get useful results.

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