How to Use the Cin7 Core MCP in LlamaIndex

Q: Can LlamaIndex index live stock levels from Cin7 Core?

Yes, you can set up a pipeline that calls getskustockstatus and indexes the returned quantities. This lets your RAG setup answer questions about real-time stock levels with zero lag.

Q: How do I limit which Cin7 Core tools LlamaIndex can access?

You can use the allowedtools filter during initialization. This lets you restrict your LlamaIndex agent to only read-only MCP tools like getproductdetails while blocking order modification endpoints.

Q: Does LlamaIndex support resource loading from this MCP Server?

Yes, by setting includeresources=True in your tool specification, LlamaIndex can directly read exposed data resources alongside executing active tools.

Index Cin7 Core inventory, sales, and supplier data directly into LlamaIndex vector stores for semantic search.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Cin7 Core MCP to LlamaIndex

Create your Vinkius account to connect Cin7 Core 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.

GDPR Free for Subscribers

Setup Cin7 Core with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Cin7 Core Inventory into Vector Stores

The `list_inventory_products` tool lets your LlamaIndex agent pull your entire Cin7 Core product catalog, including SKUs, categories, and base pricing. Rather than just viewing this raw data, the LlamaIndex framework indexes these Cin7 Core product details into a local vector store for semantic search. This setup means your LlamaIndex agent can search for 'heavy winter gear' and find the exact matches by running semantic queries over the indexed Cin7 Core descriptions. It bypasses rigid keyword matching by combining the MCP Server output with your LlamaIndex vector index.

Build RAG Pipelines on Cin7 Core Order History

The `list_sales_orders` tool provides detailed metadata about Cin7 Core customer orders, including fulfillment statuses and total order values. Your LlamaIndex RAG application can index this historical Cin7 Core order data alongside your external shipping policies. When a user asks about delayed shipments, the LlamaIndex agent queries the index to find matching sales records and cross-references them with the text documents. This grounds your LlamaIndex agent's answers in actual Cin7 Core ERP data, preventing hallucinations about order statuses.

Query Supplier Profiles Semantically in LlamaIndex

The `list_crm_suppliers` tool retrieves vendor profiles, primary contacts, and payment terms from your Cin7 Core database. LlamaIndex ingests these supplier records, letting you run natural language queries across your Cin7 Core supply chain. You can ask 'Which European suppliers support net-30 terms?' and the LlamaIndex agent will locate the correct records. It uses the MCP tool to fetch the live Cin7 Core data, then applies LlamaIndex semantic search to pinpoint the vendors matching your criteria.

Setup guide

Set up Cin7 Core 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 Cin7 Core 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 Cin7 Core tools.",
)
response = await agent.run("List recent Cin7 Core data")

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 Cin7 Core. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cin7 Core now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cin7 Core MCP in LlamaIndex

Use the llama-index-tools-mcp package to initialize the basic client. Wrap it in McpToolSpec and call to_tool_list_async to get a list of tools ready for your FunctionAgent.

Yes, you can set up a pipeline that calls get_sku_stock_status and indexes the returned quantities. This lets your RAG setup answer questions about real-time stock levels with zero lag.

You can use the allowed_tools filter during initialization. This lets you restrict your LlamaIndex agent to only read-only MCP tools like get_product_details while blocking order modification endpoints.

Yes, by setting include_resources=True in your tool specification, LlamaIndex can directly read exposed data resources alongside executing active tools.

All API requests through the MCP Server run within an ephemeral V8 sandbox managed by Vinkius. This means your sensitive customer profiles and transaction histories are never cached or used to train public models.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript