How to Use the Zentitle2 MCP in AutoGen

Q: Can the MCP Server help debate license provisioning with Zentitle2 and AutoGen?

Yes. Agents can simulate the entire lifecycle by calling createentitlement followed by getcustomerdetails, debating if this action is necessary or premature.

Facilitate consensus-driven decisions about licenses with AutoGen and Zentitle2.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Zentitle2 MCP to AutoGen

Create your Vinkius account to connect Zentitle2 to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Zentitle2 with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debating Customer Access via MCP Server

In a multi-agent debate, one agent can use `list_customers` to gather all known users. A second agent then uses `get_customer_details` to verify the active status of those accounts. The agents negotiate which customers require immediate attention based on the data. This conversational flow ensures that multiple perspectives—like compliance and finance—can weigh in using real-time data retrieved from Zentitle2's tools.

Reviewing Licensing Status with AutoGen

An agent can initiate the process by listing all available entitlements via `list_entitlements`. A second agent then uses `get_entitlement_details` to check for specific limitations or expiration dates. They debate whether the current setup meets compliance needs. This structured discussion allows you to converge on a decision, like recommending a new product purchase based on gaps found by calling `list_products`.

Managing Codes in Consensus with AutoGen

Agents can debate the best way to handle a code issue. One agent might run `get_activation_code_details` for an expired key, while another runs `list_activation_codes` to check for alternatives. The final decision is based on comparing these two datasets. When building complex workflows, AutoGen makes sure that every relevant tool, like `create_activation_code`, is considered and validated by multiple virtual experts before execution.

Setup guide

Set up Zentitle2 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 Zentitle2 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="Zentitle2_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zentitle2 data")
print(result.messages[-1].content)

Get your connection token →

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="Zentitle2_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zentitle2 data")
    print(result.messages[-1].content)

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 Zentitle2. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Zentitle2 now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Zentitle2 MCP in AutoGen

AutoGen uses the MCP tools to fuel its debate. Agents call tools like `list_products` and feed the output into their discussion, allowing them to challenge each other's assumptions based on real data.

You are debating structured licensing metadata: customer names, entitlement IDs, product SKUs, activation codes. The agents process these specific object types to reach a conclusion.

Yes. Agents can simulate the entire lifecycle by calling `create_entitlement` followed by `get_customer_details`, debating if this action is necessary or premature.

It's designed for it. The tools provide the raw data points that multiple specialized agents need to discuss and reach a consensus decision on your behalf.

The server touches specific licensing metadata: customer names, entitlement IDs, product SKUs, activation codes. The agents only see the necessary subset of this information during their debate.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript