Winevybe MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Winevybe as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Winevybe. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Winevybe?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Winevybe MCP Server
Connect your Winevybe developer account to any AI agent and take full control of sommelier intelligence and wine inventory tracking through natural conversation.
LlamaIndex agents combine Winevybe tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Sommelier Recommender — Query for specific wine pairings and tasting notes magically linked behind intelligent algorithms
- Inventory Management — Read and append bottle quantities to persistent user cellars safely via automated workflows
- Vintage Auditing — Cross-reference a specific year to see if the region's climate conditions yielded good harvests
- Pricing Comparisons — Compare distinct bottles instantly to analyze pricing versus critical community reception
- Vineyard Profiling — Retrieve the underlying history and details of major worldwide producers and regions
The Winevybe MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Winevybe to LlamaIndex via MCP
Follow these steps to integrate the Winevybe MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Winevybe
Why Use LlamaIndex with the Winevybe MCP Server
LlamaIndex provides unique advantages when paired with Winevybe through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Winevybe tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Winevybe tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Winevybe, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Winevybe tools were called, what data was returned, and how it influenced the final answer
Winevybe + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Winevybe MCP Server delivers measurable value.
Hybrid search: combine Winevybe real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Winevybe to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Winevybe for fresh data
Analytical workflows: chain Winevybe queries with LlamaIndex's data connectors to build multi-source analytical reports
Winevybe MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Winevybe to LlamaIndex via MCP:
add_wine_to_cellar
Add a purchased bottle into a users virtual cellar tracker
compare_wines
Generate a side-by-side contrast of two bottles
get_pairings
Get food pairing recommendations for a specific wine
get_region_info
Retrieve details about wine-making appellations
get_reviews
Get community tasting reviews and ratings
get_user_cellar
Examine the inventory of an authenticated users wine cellar
get_vintage_scores
Get an overview of harvest qualities by year
get_wine_detail
Get profound tasting notes and stats on a specific wine
get_winery_info
Get profiles of specific vineyards and producers
search_wines
Search the Winevybe database for specific bottles
Example Prompts for Winevybe in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Winevybe immediately.
"Search for details on the Screaming Eagle Cabernet Sauvignon."
"Compare wine 4902 and wine 5910."
"Check my virtual cellar inventory."
Troubleshooting Winevybe MCP Server with LlamaIndex
Common issues when connecting Winevybe to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpWinevybe + LlamaIndex FAQ
Common questions about integrating Winevybe MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Winevybe with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Winevybe to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.