How to Use the Alpic MCP in AutoGen

Q: How do I expose Alpic tools to an AutoGen agent?

Install autogen-extmcp and use mcpservertools with your server URL. Pass the resulting tool list to your AssistantAgent to let it start managing infrastructure.

Q: Can AutoGen agents collaborate to debug an Alpic deployment?

Yes, one agent can fetch logs using getdeploymentlogs while another agent analyzes the output. They share findings in the group chat to resolve the issue.

Q: How do AutoGen agents handle Alpic MCP Server team structures?

Agents use listteams to identify your account structure, then query listprojects to map out active work. This allows them to route deployment tasks to the correct team environment.

Q: How do I test local changes before publishing to the registry?

Run your local server and use gettunnelticket to expose it. Once verified, your agents can call publishtoregistry to make the server public.

Let your AutoGen agents debate and coordinate Alpic MCP server deployments and configuration tasks.

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Setup Alpic with AutoGen

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Multi-agent coordination for deployments

`deploy_environment` triggers an asynchronous build that your deployment agent monitors. Meanwhile, a QA agent can watch the status using `get_deployment` to verify stability. If the build fails, the agents discuss the logs fetched by `get_deployment_logs`. They collaborate to fix configuration errors without needing your direct intervention.

Manage Alpic configurations with AutoGen agents

`add_variable` allows your config agent to inject new environment variables. Before doing so, a security agent can audit the key using `list_variables` to ensure it doesn't overwrite critical production settings. This multi-agent review prevents accidental configuration overrides. The agents only execute the write operation once they reach a consensus on the safety of the change.

Safe project deletion workflows

`delete_project` permanently removes a project, which requires absolute certainty. Your admin agent coordinates with a supervisor agent to double-check the project ID first. By requiring a multi-agent sign-off, you avoid catastrophic mistakes. The tools are only executed when all safety checks in the agent group return positive results.

Setup guide

Set up Alpic 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 Alpic 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="Alpic_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Alpic. 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 Alpic MCP in AutoGen

Install `autogen-ext[mcp]` and use `mcp_server_tools` with your server URL. Pass the resulting tool list to your `AssistantAgent` to let it start managing infrastructure.

Yes, one agent can fetch logs using `get_deployment_logs` while another agent analyzes the output. They share findings in the group chat to resolve the issue.

Agents use `list_teams` to identify your account structure, then query `list_projects` to map out active work. This allows them to route deployment tasks to the correct team environment.

Run your local server and use `get_tunnel_ticket` to expose it. Once verified, your agents can call `publish_to_registry` to make the server public.

Tunnel tickets and raw deployment logs are stored in ephemeral, short-lived storage. They are automatically purged after use, ensuring your local code execution paths and build outputs remain private.

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