How to Use the Indemnification Exposure Calculator MCP in AutoGen

Q: How can I make AutoGen agents use the Indemnification Exposure Calculator?

You provide the tools from this MCP to your AssistantAgent during setup. The agents in your group chat can then choose to call any of the calculation tools to get data for their arguments.

Let AutoGen agents debate contract risk, using the Indemnification Exposure Calculator to ground their arguments in hard numbers.

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Setup Indemnification Exposure Calculator with AutoGen

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Assemble Agent Teams for Risk Review

Set up a team of specialized agents to tear down a contract. One agent, the 'Risk Analyst,' uses `calculate_worst_case_exposure` to flag high-risk clauses. A second agent, the 'Deal Maker,' can argue for accepting the risk based on the business opportunity. The entire debate is grounded in real numbers from this MCP. It's a structured argument, not a vague discussion.

Challenge and Verify Calculations with AutoGen

Don't just trust the first answer. An 'Analyst' agent can run `calculate_standard_cap`, and a second 'Auditor' agent can independently call `evaluate_carveout_impact` to see if the standard cap is misleading. The agents converse in a group chat, presenting data from the tools as evidence. They argue until they reach a consensus on the contract's true financial exposure.

Automate Contract Negotiation Prep

Use an AutoGen conversation to simulate a negotiation before you ever talk to the other side. Your agent team can discuss a counter-offer, using the MCP Server's tools to model different scenarios and their financial impact. For example: 'What if we accept their liability cap but reject the data breach carve-out?' The agents use the tool outputs to determine the strongest, data-backed negotiating position.

Setup guide

Set up Indemnification Exposure Calculator MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Indemnification Exposure Calculator tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Indemnification Exposure Calculator_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Indemnification Exposure Calculator data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Indemnification Exposure Calculator_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Indemnification Exposure Calculator data")
    print(result.messages[-1].content)

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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 Indemnification Exposure 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 Indemnification Exposure Calculator MCP in AutoGen

You provide the tools from this MCP to your `AssistantAgent` during setup. The agents in your group chat can then choose to call any of the calculation tools to get data for their arguments.

Yes. In a multi-agent conversation, any agent with access to the tools can call them. This allows one agent to present a number that another agent can then challenge or verify with a different calculation.

AutoGen models a debate. Instead of one agent making a call, you have a team that challenges assumptions and uses this MCP's hard numbers to justify their positions. This leads to a more robust and vetted conclusion.

The conversation history shows which agent called which tool, with what inputs, and what the result was. This provides a clear audit trail of how the team arrived at its final risk assessment.

The MCP is stateless and only sees the inputs your agents send with each tool call, like contract amounts or carve-out terms. It never has access to the agent's conversation history or any other internal data.

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