Open Beauty Facts MCP Server for LlamaIndex 2 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Open Beauty Facts 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 Open Beauty Facts. "
"You have 2 tools available."
),
)
response = await agent.run(
"What tools are available in Open Beauty Facts?"
)
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 Open Beauty Facts MCP Server
Equip your AI agent with the definitive open database for cosmetic products through the Open Beauty Facts MCP server. This integration provides real-time access to a collaborative database of beauty products from around the world. Your agent can search for cosmetics by name or barcode, retrieve detailed lists of ingredients (INCI), and identify potential allergens or restricted substances. Whether you are auditing your skincare routine, researching cosmetic formulations, or verifying product claims, your agent acts as a dedicated personal care specialist through natural conversation.
LlamaIndex agents combine Open Beauty Facts tool responses with indexed documents for comprehensive, grounded answers. Connect 2 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
- Product Lookup — Find cosmetic products by name, brand, or EAN/UPC barcode.
- Ingredient Analysis — Retrieve the complete INCI list for thousands of beauty and hygiene products.
- Allergen Detection — Identify potential allergens and irritants in specific formulations.
- Brand Auditing — Explore the product portfolios of global cosmetic brands.
The Open Beauty Facts MCP Server exposes 2 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 Open Beauty Facts to LlamaIndex via MCP
Follow these steps to integrate the Open Beauty Facts 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 2 tools from Open Beauty Facts
Why Use LlamaIndex with the Open Beauty Facts MCP Server
LlamaIndex provides unique advantages when paired with Open Beauty Facts through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Open Beauty Facts tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Open Beauty Facts tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Open Beauty Facts, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Open Beauty Facts tools were called, what data was returned, and how it influenced the final answer
Open Beauty Facts + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Open Beauty Facts MCP Server delivers measurable value.
Hybrid search: combine Open Beauty Facts real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Open Beauty Facts 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 Open Beauty Facts for fresh data
Analytical workflows: chain Open Beauty Facts queries with LlamaIndex's data connectors to build multi-source analytical reports
Open Beauty Facts MCP Tools for LlamaIndex (2)
These 2 tools become available when you connect Open Beauty Facts to LlamaIndex via MCP:
get_beauty_product
Get cosmetic product details by barcode
search_beauty_products
Search for beauty products by category
Example Prompts for Open Beauty Facts in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Open Beauty Facts immediately.
"Search for cosmetic products from the brand 'Nivea'."
"What are the ingredients in the product with barcode '4005900130778'?"
"Identify potential allergens in 'La Roche-Posay Anthelios'."
Troubleshooting Open Beauty Facts MCP Server with LlamaIndex
Common issues when connecting Open Beauty Facts to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpen Beauty Facts + LlamaIndex FAQ
Common questions about integrating Open Beauty Facts 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 Open Beauty Facts with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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 Open Beauty Facts to LlamaIndex
Get your token, paste the configuration, and start using 2 tools in under 2 minutes. No API key management needed.