How to Use the DoiT MCP in AutoGen

Q: How do AutoGen agents collaborate on DoiT cost anomalies?

You set up a group chat where one agent runs listcostanomalies to find issues. Another agent uses getassetdetails to investigate the culprit. They discuss the findings to reach a consensus on remediation.

Q: Can AutoGen manage multi-cloud accounts using DoiT?

Yes. The agents use listconnectedcloudaccounts to discover all AWS, GCP, and Azure accounts. They can then query billing summaries across all of them simultaneously.

Put your AutoGen agents in a room to debate, analyze, and resolve DoiT cloud cost spikes automatically.

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Setup DoiT with AutoGen

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Resolve cost spikes via multi-agent debate

`list_critical_cost_spikes` triggers a collaborative investigation between your specialized AutoGen agents. While one agent flags the spike, another immediately analyzes the source to see if it's a false positive. They debate the urgency first, pulling real-time data from the MCP Server before presenting a unified recommendation. This prevents panic alerts over planned database migrations.

Coordinate budget tracking with an AutoGen MCP Server

`list_cost_budgets` provides the baseline financial boundaries that your budget-monitoring agent enforces during conversations. Comparing these baselines against actual spend allows the agent to warn others when limits are close. When a budget is broken, the agent pulls data from `list_exceeded_cost_budgets`. These agents then negotiate which non-essential resources can be paused.

Audit cloud accounts through collaborative workflows

`list_connected_cloud_accounts` maps out the entire organizational footprint for your auditing agents. No rogue accounts slip through the cracks when your auditing agents verify each connected account systematically. Using `get_doit_account_metadata` confirms ownership and billing details. The entire audit happens in the background without manual oversight.

Setup guide

Set up DoiT 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 DoiT 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="DoiT_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about DoiT MCP in AutoGen

First, install the extension package using pip install -U "autogen-ext[mcp]". Use mcp_server_tools with your Vinkius HTTP endpoint to fetch the tools. Finally, pass the tool list directly to your AssistantAgent.

You set up a group chat where one agent runs list_cost_anomalies to find issues. Another agent uses get_asset_details to investigate the culprit. They discuss the findings to reach a consensus on remediation.

Yes. The agents use list_connected_cloud_accounts to discover all AWS, GCP, and Azure accounts. They can then query billing summaries across all of them simultaneously.

The adapter supports both stdio and Streamable HTTP transports. For Vinkius-hosted servers, you will use the Streamable HTTP client configuration.

Yes. The MCP Server only handles organizational metadata and cloud account identifiers through secure V8 isolates. Vinkius never caches your DoiT API keys or billing details, ensuring complete isolation.

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