How to Use the Wine Pairing & Sommelier MCP in AutoGen

Make your AI client debate complex choices with AutoGen; superior Wine Pairing & Sommelier logic.

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Connect Wine Pairing & Sommelier MCP to AutoGen

Create your Vinkius account to connect Wine Pairing & Sommelier 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 Wine Pairing & Sommelier with AutoGen

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Claude

Perplexity

Achieve consensus on pairings

You set up multiple agents—a 'Budget Agent,' a 'Flavor Profile Agent,' and the 'Sommelier Agent.' They debate which wine to suggest for steak. The final, negotiated decision uses `get_wine_pairing` with consideration of price points and flavor notes.

Resolve complex pairing conflicts

One agent might call `get_dish_for_wine` suggesting a Pinot Noir, but another critiques it based on the main ingredients. The agents discuss this conflict until they converge on the best recommendation for your specific needs.

Simulate expert consultation

The system can run a full 'consultation' where agents take turns: one describes a wine using `get_wine_description`, another suggests pairings, and a third agent checks the market price via `recommend_wines`. The final output is the consensus decision.

Setup guide

Set up Wine Pairing & Sommelier 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 Wine Pairing & Sommelier 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="Wine Pairing & Sommelier_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Wine Pairing & Sommelier 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="Wine Pairing & Sommelier_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Wine Pairing & Sommelier 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 Spoonacular Wine. 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 Wine Pairing & Sommelier MCP in AutoGen

You define roles for your agents—like a 'Chef' and a 'Somm.' The system then forces them to debate the best pairing. This multi-agent discussion ensures that all constraints (price, flavor, dish) are considered before a final suggestion is made.

Yes. It moves beyond simple tool execution. The agents argue and refine the answer until they reach consensus, making the final pairing recommendation much more robust than a single API call would allow.

The process handles dish names, wine product names, detailed flavor descriptions, associated ratings, and price ranges. All these pieces of information are debated among the agents.

You get a reasoned decision supported by multiple viewpoints. The output isn't just a list; it’s a conclusion achieved through negotiation among specialized AI agents.

It's highly dependable because the consensus mechanism forces checks and balances. The final answer has passed through multiple critical perspectives before being presented to you.

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