How to Use the Umbrellar MCP in AutoGen

Q: Can I use Umbrellar to verify policies within AutoGen?

You can absolutely use validatepolicy as a critical step in your agent debate. One agent can challenge an assumption by calling this tool, forcing the conversation toward verifiable facts.

Q: Does Umbrellar handle product syncing for AutoGen workflows?

Yes, the syncproducts tool allows agents to keep external systems and the MCP Server's product lists consistent. An agent can run this before starting a policy review.

Drive consensus decisions on Umbrellar service data using AutoGen.

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Connect Umbrellar MCP to AutoGen

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Setup Umbrellar with AutoGen

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Automated Claim Review and Debate

Set up a multi-agent system where one agent calls `check_product_eligibility` while another verifies the required policy with `get_policy`. These agents debate whether the current product meets all necessary criteria for service. The final decision on claim submission isn't just executed; it results from a structured negotiation between specialized roles.

Negotiating Product Registration Status

You can deploy an agent pair: one handles product registration (`register_product`), and the other reviews existing records using `list_policies`. They argue until they reach consensus on whether a new product needs immediate policy creation. This mimics real-world governance, requiring deliberation before any action is taken.

Verifying Policy Completeness through Debate

Use Umbrellar's tools to check for data gaps. An 'Audit Agent' calls `list_warranties`, while a 'Compliance Agent' checks `validate_policy` against those results. The agents argue until the policy coverage is verified. The outcome is a high-confidence decision that multiple perspectives signed off on.

Setup guide

Set up Umbrellar 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 Umbrellar 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="Umbrellar_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Umbrellar 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="Umbrellar_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Umbrellar 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 Umbrellar. 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 Umbrellar MCP in AutoGen

AutoGen agents can collaborate by having one agent gather data using `get_claim` and another agent analyzing that output against the rules provided by `get_warranty`. This collaborative approach ensures all angles are considered.

The server provides highly structured service data, including product eligibility flags, policy name/ID pairs, and formatted claim submission details. These inputs drive the agents' arguments.

You can absolutely use `validate_policy` as a critical step in your agent debate. One agent can challenge an assumption by calling this tool, forcing the conversation toward verifiable facts.

Yes, the `sync_products` tool allows agents to keep external systems and the MCP Server's product lists consistent. An agent can run this before starting a policy review.

It handles structured records like warranty plans, registered products for OEM coverage, and the detailed metadata required to submit or update service claims. This depth of data allows agents to debate accurately.

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