VineRadar MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add VineRadar as an MCP tool provider through the 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 VineRadar. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in VineRadar?"
)
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 VineRadar MCP Server
Empower your AI agent to orchestrate your entire wine research and vineyard auditing workflow with VineRadar, the comprehensive platform for global wine data. By connecting VineRadar to your agent, you transform complex varietal searches into a natural conversation. Your agent can instantly search for specific wines, audit vineyard locations, and retrieve detailed vintage metadata without you ever touching a wine app. Whether you are building a personal cellar or conducting market research on varietals, your agent acts as a real-time sommelier, ensuring your data is always detailed and well-categorized.
LlamaIndex agents combine VineRadar tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the 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
- Wine Auditing — Search for thousands of wines by name or keyword and retrieve detailed metadata, including ratings and vintages.
- Vineyard Oversight — Browse vineyard profiles by location to maintain a clear view of regional wine production.
- Varietal Discovery — Query wine varietals to understand the technological and regional distribution of specific grape types instantly.
- Vintage Intelligence — Retrieve full details for specific wine IDs to assist in deep-dive collection audits.
- Market Monitoring — List all supported varietals in the VineRadar catalog to identify trending wine themes in real-time.
The VineRadar MCP Server exposes 6 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 VineRadar to LlamaIndex via MCP
Follow these steps to integrate the VineRadar 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 6 tools from VineRadar
Why Use LlamaIndex with the VineRadar MCP Server
LlamaIndex provides unique advantages when paired with VineRadar through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine VineRadar tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain VineRadar tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query VineRadar, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what VineRadar tools were called, what data was returned, and how it influenced the final answer
VineRadar + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the VineRadar MCP Server delivers measurable value.
Hybrid search: combine VineRadar real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query VineRadar 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 VineRadar for fresh data
Analytical workflows: chain VineRadar queries with LlamaIndex's data connectors to build multi-source analytical reports
VineRadar MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect VineRadar to LlamaIndex via MCP:
check_api_status
Check if the VineRadar API is operational
get_vineyard_details
Get full details for a specific vineyard by ID
get_wine_details
Get full details for a specific wine by ID
list_wine_varietals
List all wine varietals supported by VineRadar
search_vineyards
Search for vineyards by location
search_wines
Search for wines by name or keyword
Example Prompts for VineRadar in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with VineRadar immediately.
"Search for 'Cabernet Sauvignon' wines using VineRadar."
"Find vineyards in 'Napa Valley'."
"What are the details for wine ID 12345?"
Troubleshooting VineRadar MCP Server with LlamaIndex
Common issues when connecting VineRadar to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVineRadar + LlamaIndex FAQ
Common questions about integrating VineRadar 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 VineRadar 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 VineRadar to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.