How to Use the Wine Pairing & Sommelier MCP in CrewAI

Q: Can I get pricing information when running Wine Pairing & Sommelier with CrewAI?

Yes. Both getwinepairing and recommendwines return specific product data that includes prices and ratings, making it actionable for e-commerce or inventory systems.

Q: If I need a detailed description for a wine using Wine Pairing & Sommelier with CrewAI?

You assign an agent to use the getwinedescription tool. This agent researches the details, and another agent can then take that detailed text and summarize it into marketing copy.

Q: What is the best way to structure Wine Pairing & Sommelier with CrewAI?

Structure a crew where one agent calls getdishforwine (research), passes the result to a second agent that calls getwinepairing (analysis), and a third agent compiles the final output (action).

Run multi-agent teams for Wine Pairing & Sommelier expertise with CrewAI's autonomous MCP Server operations.

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Create your Vinkius account to connect Wine Pairing & Sommelier to CrewAI 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 CrewAI

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Perplexity

Automated Dish Recommendations with the MCP Server

CrewAI excels at collaboration. You can assign one agent to research pairings using `get_dish_for_wine`. A second, specialized agent then takes that output and uses `get_wine_pairing` to find matching bottles. The roles work together sequentially. This approach allows you to build a complex operation where the first agent researches the food side while the moderator agent handles the final product recommendation list.

Specialized Wine Discovery using CrewAI

You can set up multiple agents for wine discovery. Agent A reads the general knowledge of a wine type via `get_wine_description`. Agent B then uses that description to call `recommend_wines` and generate specific, high-rated product suggestions. This is role specialization in action. The shared memory keeps track of all inputs, ensuring that when Agent C summarizes the findings, it has access to both the wine details and the suggested products.

Running Autonomous Pairing Operations

Use a hierarchical crew structure. The main agent receives a dish (e.g., 'salmon'). It hands this off to an expert pairing agent, which uses `get_wine_pairing` to get product suggestions, ratings, and prices. A final monitor agent then compiles the full report. This makes the system fully autonomous, handling everything from initial input gathering to structured output generation without human intervention.

Setup guide

Set up Wine Pairing & Sommelier MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Wine Pairing & Sommelier tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Wine Pairing & Sommelier Analyst",
    goal="Access and analyze Wine Pairing & Sommelier data via MCP.",
    backstory="Expert analyst with direct Wine Pairing & Sommelier access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Wine Pairing & Sommelier transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Wine Pairing & Sommelier Analyst",
        goal="Access and analyze Wine Pairing & Sommelier data via MCP.",
        backstory="Expert analyst with direct Wine Pairing & Sommelier access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Wine Pairing & Sommelier transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

You assign specialized roles: one agent researches the pairing via `get_wine_pairing`, and another compiles the final report using all gathered product data. The crew runs these tools in a defined sequence.

Yes. Both `get_wine_pairing` and `recommend_wines` return specific product data that includes prices and ratings, making it actionable for e-commerce or inventory systems.

The server only handles public wine pairing data—product names, ratings, and prices. It doesn't require any sensitive personal information from the user or customer.

You assign an agent to use the `get_wine_description` tool. This agent researches the details, and another agent can then take that detailed text and summarize it into marketing copy.

Structure a crew where one agent calls `get_dish_for_wine` (research), passes the result to a second agent that calls `get_wine_pairing` (analysis), and a third agent compiles the final output (action).

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