How to Use the InnoVint MCP in AutoGen
Let autonomous AutoGen MCP agents debate cellar actions and verify InnoVint chemistry before making final decisions.
Works with every AI agent you already use
…and any MCP-compatible client
Connect InnoVint MCP to AutoGen
Create your Vinkius account to connect InnoVint 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.
Run consensus-driven cellar operations with AutoGen
You call `list_actions` to let a specialized AutoGen cellar agent analyze the historical treatments applied to a stalling ferment. A separate chemistry agent then challenges those findings by pulling the latest lab results. This multi-agent debate ensures that no physical action is recommended blindly. By forcing the agents to negotiate, you get a highly scrutinized cellar recommendation that respects both historic actions and current chemistry.
Validate additive additions using AutoGen MCP Server tools
Your planning agent executes `list_additives` to determine the chemical limits and standard dosages for a target wine lot. Before writing the cellar work order, a compliance agent reviews the proposed additions against legal thresholds. If the proposed sulfur dioxide addition exceeds regulatory limits, the compliance agent flags the error and demands a recalculation. This automated check prevents costly compliance mistakes in your production logs.
Coordinate multi-winery vessel allocation
You run `list_vessels` to let your logistics agent map out available tank space for the upcoming harvest. A facility agent simultaneously queries `list_wineries` to coordinate intake schedules across different physical locations. The agents negotiate the best routing plan, balancing tank capacities against truck arrival times. You get a coordinated cellar plan without manual scheduling conflicts.
Set up InnoVint 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes InnoVint tools and returns structured results.
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="InnoVint_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent InnoVint data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="InnoVint_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent InnoVint data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by InnoVint. 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 InnoVint MCP in AutoGen
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