How to Use the Cornershop MCP in LlamaIndex

Index live grocery data into LlamaIndex to build RAG apps that ground your delivery logic in real-time store availability.

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Connect Cornershop MCP to LlamaIndex

Create your Vinkius account to connect Cornershop 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 Cornershop with LlamaIndex

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Vectorize store data for LlamaIndex

Turn `list_stores` and `list_store_aisles` results into searchable knowledge. LlamaIndex stores this output in your vector database, allowing your agent to query store layouts semantically. Stop guessing which aisle holds an item. Your agent references the indexed data to build accurate carts before ever calling the live order tools.

Query product availability with LlamaIndex

Use `search_products` to feed your RAG pipeline with current pricing and SKU availability. The model gains instant context about what is actually on the shelf. By grounding the agent in this live data, you prevent hallucinations about product prices or stock. The agent only suggests what it can confirm through the index.

Manage order history via MCP

The server provides `list_orders` and `get_order` to populate your index with past delivery details. You can query your own history to find common preferences or repeat orders. This turns your order logs into a personal shopping assistant. LlamaIndex retrieves relevant past patterns to speed up the creation of new delivery requests.

Setup guide

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

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Common questions about Cornershop MCP in LlamaIndex

You use the tool spec to fetch data and then pass the output into your vector store indexing pipeline. This makes your live grocery data queryable alongside your static documents.

Absolutely. By indexing the output of the product search tools, the agent relies on real-time data instead of its internal training set for price and availability questions.

Yes. You can restrict the agent to only use specific tools like `search_products` or `get_store` to keep the context focused on grocery tasks.

You re-run the tool call to update your index. LlamaIndex keeps your RAG system synchronized with the latest store information from the API.

Your API tokens and order details are handled locally. We use ephemeral, isolated environments for every request, ensuring your sensitive grocery history remains strictly under your control.

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