How to Use the Vultr MCP in AutoGen

Debate infrastructure choices using multiple agents with Vultr and AutoGen.

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Connect Vultr MCP to AutoGen

Create your Vinkius account to connect Vultr 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 Vultr with AutoGen

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Perplexity

Negotiating Hardware Deployment

You can set up a debate between two agents: one pushing for maximum performance (running `reboot_bare_metal`) and another prioritizing stability. They argue over whether to check the account first using `get_account` before proceeding.

Security Audit Consensus

A dedicated security agent can run `list_api_keys` while a performance agent checks resource availability with `list_bare_metals`. The agents debate which risk takes priority, leading to a consensus decision on whether to proceed or halt the instance (`halt_bare_metal`).

Resource Provisioning Debate

One agent might detect an IP conflict using `get_bare_metal_ipv4` and propose setting a reverse DNS. A second agent then challenges that proposal, forcing the system to debate whether the change is necessary or if simply running `reinstall_bare_metal` is better.

Setup guide

Set up Vultr 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 Vultr 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="Vultr_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

AutoGen's strength is debate. It lets you model a scenario where multiple specialized agents—like 'Security' and 'Ops'—discuss the best action based on real Vultr data, rather than just following a linear script.

It handles complex, non-obvious choices. For example, should the system delete an old bare metal (`delete_bare_metal`) or just halt it? The agents argue until they converge on a decision.

The agents exchange messages and challenge each other's proposed tool calls. The system only moves forward once the group reaches a debated agreement—a true multi-agent conversation.

Yes, by using the MCP Server tools, your agents can interact with various resource types, from listing applications (`list_applications`) to managing backups (`list_backups`).

The server touches key identifiers and access details. When agents run `get_account`, they are dealing with the core account information, which serves as a critical piece of shared context for their debate.

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