How to Use the Wine-Searcher MCP in CrewAI

Q: How do I search for wine prices using Wine-Searcher and CrewAI?

You let an agent use marketprice. The crew can then assign a secondary agent to analyze that pricing data against historical trends. This multi-agent approach provides more context than a single tool call.

Q: Can Wine-Searcher and CrewAI research producer details?

Yes, the team can use producerinfo. One agent collects the raw winery data while another formats it into a comparison report. This is ideal for large-scale brand analysis.

Q: What if I need to check wine specifics using Wine-Searcher and CrewAI?

The winecheck tool provides the core data. You can build a crew where one agent runs the initial lookup, and another summarizes the results by focusing only on the grape variety and region.

Q: Does Wine-Searcher and CrewAI help with regional exploration?

Absolutely. One specialized agent uses regioninfo to define the geographical parameters. Another can then use those parameters to filter results in the main wine database, providing structured exploration.

Automate wine research operations with CrewAI's specialized agent teams.

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Connect Wine-Searcher MCP to CrewAI

Create your Vinkius account to connect Wine-Searcher 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-Searcher with CrewAI

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Collaborative wine discovery

You can run a crew where one agent researches using `search_wines`, and another analyzes the results for scores and pricing. This role-based specialization means you get structured, actionable data output. The shared memory feature ensures that the initial search criteria are available to all agents when they start their analysis.

Deep grape education research

Need a full report on a specific type of grape? Assign an agent to use `grape_info` for deep details. A secondary agent can then compile this data with regional context from `region_info`, creating a comprehensive, multi-part document. This team approach handles the complex assembly of disparate pieces of information.

Automated wine viability check

Set up a sequence: Agent 1 uses `wine_check` to gather raw data (regions, grape types, pricing). Agent 2 then filters this data based on predefined rules, flagging any wines that don't meet the minimum score threshold. The monitor agent watches the entire process, escalating issues if necessary.

Setup guide

Set up Wine-Searcher 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-Searcher tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Wine-Searcher 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="Wine-Searcher Analyst",
        goal="Access and analyze Wine-Searcher data via MCP.",
        backstory="Expert analyst with direct Wine-Searcher access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Wine-Searcher 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 Wine-Searcher. 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-Searcher MCP in CrewAI

You let an agent use `market_price`. The crew can then assign a secondary agent to analyze that pricing data against historical trends. This multi-agent approach provides more context than a single tool call.

Yes, the team can use `producer_info`. One agent collects the raw winery data while another formats it into a comparison report. This is ideal for large-scale brand analysis.

The `wine_check` tool provides the core data. You can build a crew where one agent runs the initial lookup, and another summarizes the results by focusing only on the grape variety and region.

Absolutely. One specialized agent uses `region_info` to define the geographical parameters. Another can then use those parameters to filter results in the main wine database, providing structured exploration.

The MCP Server handles public market research data—think global pricing, critic scores, grape varieties, and producer names. Your agents process this external, non-private information.

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