How to Use the Indemnification Exposure Calculator MCP in CrewAI

Deploy specialized legal agents using CrewAI to audit contract liability and enforce risk policies autonomously.

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Connect Indemnification Exposure Calculator MCP to CrewAI

Create your Vinkius account to connect Indemnification Exposure Calculator 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 Indemnification Exposure Calculator with CrewAI

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Autonomous Liability Screening

Assign a dedicated agent to monitor and validate contract caps across your entire document repository. This frees up your counsel for more complex work. By running `calculate_standard_cap`, your research agent can flag non-compliant contracts in seconds.

Hierarchical Risk Assessment Teams

Use a team of agents to perform deep dives into potential financial exposure. This ensures every contract is analyzed from multiple perspectives before approval. Agents collaborate by sharing the output of `calculate_worst_case_exposure`, allowing the team to reach a consensus on total liability.

Proactive Carve-out Monitoring

Detect dangerous carve-out clauses before they are finalized in your agreements. This prevents accidental exposure to unlimited liability. `evaluate_carveout_impact` provides the critical data your moderator agent needs to decide if a contract requires immediate legal intervention.

Setup guide

Set up Indemnification Exposure Calculator 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 Indemnification Exposure Calculator tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Indemnification Exposure Calculator 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 →

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="Indemnification Exposure Calculator Analyst",
        goal="Access and analyze Indemnification Exposure Calculator data via MCP.",
        backstory="Expert analyst with direct Indemnification Exposure Calculator access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Indemnification Exposure Calculator 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 →

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