How to Use the Wine Pairing & Sommelier MCP in LlamaIndex

Ground your AI client's knowledge base with LlamaIndex; better Wine Pairing & Sommelier answers every time.

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Connect Wine Pairing & Sommelier MCP to LlamaIndex

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

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Search historical wine pairings

The output from `get_wine_pairing` doesn't just disappear. LlamaIndex indexes the entire result set into a searchable knowledge base. You can then query past sessions—for example, 'What was the pairing suggested for chocolate last month?'—and get an answer grounded in actual API data.

Build RAG systems with MCP Server pairings

You combine live API calls with your own documents. If you upload a restaurant's seasonal menu, LlamaIndex can index that alongside the results from `get_dish_for_wine`. This lets your agent answer questions like 'What wine pairs best with our new spring pasta?' using both data sources.

Query detailed wine descriptions

Instead of just getting a description, LlamaIndex turns it into a queryable resource. You can index all results from `get_wine_description` and ask semantic questions like 'Describe Riesling's flavor profile when aged in oak,' letting your agent pull the answer directly from the stored knowledge.

Setup guide

Set up Wine Pairing & Sommelier MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Wine Pairing & Sommelier MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Wine Pairing & Sommelier tools.",
)
response = await agent.run("List recent Wine Pairing & Sommelier data")

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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 LlamaIndex

It indexes the results—the pairings, descriptions, ratings, and prices—into a vector store. This means your agent can retrieve context from past calls or external documents to give highly specific answers.

Yes. By grounding the AI in indexed data, it eliminates guesswork. If a pairing was recommended before, you can query that exact historical record rather than relying on generalized model knowledge.

It stores the full outputs: product suggestions with prices and ratings from `recommend_wines`, detailed wine descriptions, and specific dish-to-wine pairings. Everything becomes part of your unified knowledge index.

The main benefit is persistent memory. Your agent can answer questions based on data from weeks ago or even combine that historical data with a new query against your own documents.

It's extremely reliable because it forces the model to reference documented API outputs. It won't hallucinate pairings or prices; it pulls them from the indexed data.

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