How to Use the Balena MCP in AutoGen

Let your AutoGen agents debate and execute Balena fleet operations with consensus-driven logic.

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Setup Balena with AutoGen

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AutoGen agents managing Balena fleets

Assign one agent to check `list_fleets` and another to verify device compatibility via `list_os_versions`. They debate the best path before committing any changes to your production environment. This negotiation prevents reckless deployments. You define the constraints, and the agents ensure every operation meets your safety standards.

Consensus on Balena device changes

Use `create_device_env_var` only after your specialized agents agree on the configuration. One agent focuses on performance, while another audits the security impact of the variable change. You get a clear record of why a specific change was made. The debate process forces the agents to justify their actions based on the API data provided.

Automated Balena release audits in AutoGen

Deploy a release auditor agent to check `list_releases` against your requirements. It discusses findings with the deployment agent to reach a final decision on whether to proceed. This setup removes the guesswork from your release cycle. You gain a deliberative system that understands the complexities of your edge infrastructure.

Setup guide

Set up Balena 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 Balena 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="Balena_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You pass the server parameters to the tool adapter provided by the extension. This exposes the Balena toolset to your agent group automatically.

Absolutely, you can instruct one agent to propose a tag update and another to challenge it. They will negotiate the correct tag name based on your fleet policy.

All Balena API interactions are encapsulated in a zero-trust sandbox. The agents only see the specific data points required for their assigned task.

The adapter handles the schema mapping for you. You define your agents, and the MCP tools are converted into compatible functions for the conversation.

You provide a single endpoint token to the server. The agents never handle your raw API keys; they only receive the results of authorized tool calls.

Use it with your favorite AI tools

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