How to Use the JD Cloud Infrastructure MCP in AutoGen

Enable multi-agent debate for JD Cloud Infrastructure operations using the AutoGen framework.

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Connect JD Cloud Infrastructure MCP to AutoGen

Create your Vinkius account to connect JD Cloud Infrastructure 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.

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Setup JD Cloud Infrastructure with AutoGen

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Multi-agent debate on JD Cloud Infrastructure

Deploy specialized agents that discuss the best way to handle `reboot_vm_instance` or `stop_vm_instance` commands. A security agent can challenge a performance agent, forcing a consensus before any action occurs. This prevents hasty decisions. By requiring deliberation, your system avoids accidental downtime caused by a single agent making an unchecked move.

Negotiated resource management

Use `list_vm_instances` and `describe_metric_data` to feed multiple agents with identical telemetry. They compare the data and agree on the optimal remediation strategy for unhealthy nodes. Conflict resolution is built into the process. Your agents negotiate the trade-offs between cost and performance, ensuring the final decision reflects the team's operational priorities.

Automated infrastructure audit in AutoGen

Task a group of agents with auditing your `list_cloud_disks` and `list_rds_instances` output. They compare findings to identify unused resources or potential security gaps. This group effort yields a clearer picture of your infrastructure. You get a consensus-based report on your cloud footprint, generated through debate between your agents.

Setup guide

Set up JD Cloud Infrastructure 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 JD Cloud Infrastructure 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="JD Cloud Infrastructure_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent JD Cloud Infrastructure 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="JD Cloud Infrastructure_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about JD Cloud Infrastructure MCP in AutoGen

Agents share the same tool definitions and respond to each other's proposals. They use the tool outputs to justify their positions before reaching a final conclusion.

Yes, once you define the conversation flow and objectives. The agents will execute the necessary tools to achieve the goal based on the logic you set.

All interaction with your cloud environment is handled via secure API calls. The agents only see the data returned by the tools, which is processed in memory during the conversation.

It integrates directly into the AssistantAgent constructor. The tools are automatically adapted for use in your multi-agent conversations without extra manual setup.

You can include a human-in-the-loop agent to review all proposed tool calls before they execute. This adds a final layer of validation to the agents' consensus.

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