How to Use the Winevybe MCP in CrewAI

Q: How does Winevybe work with CrewAI?

You assign roles: one agent handles research (e.g., getvintagescores), another analyzes the data, and a third acts on the findings—all collaborating in a single run.

Q: Can I automate checking my cellar inventory with Winevybe and CrewAI?

Yes. You can build an autonomous crew where one agent gets getusercellar and another immediately uses the list of bottles to check for missing region data using getregioninfo.

Q: What kind of information does Winevybe support via MCP Server with CrewAI?

It supports everything from deep tasting notes (getwinedetail) to general market data like vintage score overviews, making it useful for many domains.

Run autonomous Winevybe operations with CrewAI's specialized agent teams.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Winevybe MCP to CrewAI

Create your Vinkius account to connect Winevybe 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.

GDPR Free for Subscribers

Setup Winevybe with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Autonomous Market Research via MCP Server

Set up a team where one agent researches regional appellations using `get_region_info`. A second agent then takes those findings and cross-references them with specific producer profiles from `get_winery_info`. The system executes these roles sequentially, building comprehensive market reports without human input.

Winevybe Cellar Auditing using CrewAI

A moderator agent watches the session while a dedicated cellar agent runs `get_user_cellar`. The crew can then pass this inventory list to another agent that pulls corresponding community reviews using `get_reviews`. This builds an automated audit of the user's collection health.

Multi-agent Wine Comparison

You can run a crew where Agent A finds two specific bottles via `search_wines`. Agent B then calls `compare_wines` to generate the contrast report. The final agent summarizes this output into an action plan. This simulates a full sommelier consultation.

Setup guide

Set up Winevybe 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 Winevybe tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Winevybe 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 →

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="Winevybe Analyst",
        goal="Access and analyze Winevybe data via MCP.",
        backstory="Expert analyst with direct Winevybe access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Winevybe 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 Winevybe. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Winevybe now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Winevybe MCP in CrewAI

You assign roles: one agent handles research (e.g., `get_vintage_scores`), another analyzes the data, and a third acts on the findings—all collaborating in a single run.

Yes. You can build an autonomous crew where one agent gets `get_user_cellar` and another immediately uses the list of bottles to check for missing region data using `get_region_info`.

Absolutely. You can set up a specialized agent that receives wine details and calls `get_pairings` to suggest recipes, completing the task without human intervention.

It supports everything from deep tasting notes (`get_wine_detail`) to general market data like vintage score overviews, making it useful for many domains.

This server handles highly specific textual and quantitative records, including community ratings and detailed wine statistics.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python