How to Use the Sellfy MCP in LlamaIndex

Q: How do I expose Sellfy tools to LlamaIndex?

Install llama-index-tools-mcp via pip. Set up a BasicMCPClient, wrap it in McpToolSpec, and call await mcptoolspec.totoollistasync(). Feed the resulting list into your FunctionAgent.

Q: Can LlamaIndex search my Sellfy customer history?

Yes. You configure the agent to call listcustomers and getcustomerdetails. The framework indexes the buyer profiles so you can run semantic queries against your user base.

Index your Sellfy store data into LlamaIndex for semantic search and RAG.

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Connect Sellfy MCP to LlamaIndex

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Setup Sellfy with LlamaIndex

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Index Sellfy Products and Categories

The `list_products` and `get_product_details` tools pull your entire digital and physical catalog into LlamaIndex. Your setup embeds pricing, descriptions, and file metadata directly into a vector store. Now you can query your inventory using natural language. Ask your agent which products cost more than fifty dollars, and it searches the index instead of hallucinating. It cross-references the output with `list_product_categories` to give you accurate taxonomy mapping.

Query Store Subscriptions via MCP Server

The MCP Server exposes `list_subscriptions` and `get_subscription_details` so your RAG application can ingest recurring revenue data. It pulls subscriber statuses, billing cycles, and renewal dates into your searchable knowledge base. You stop guessing about churn. You just ask your application for a summary of canceled memberships this month. LlamaIndex retrieves the exact records from the API, embeds them, and returns an answer grounded in hard data.

Grounded Order Analytics

With `list_orders` and `get_order_details`, your agent fetches recent transactions and indexes the line items. You get a semantic search layer over your raw e-commerce sales. You combine this with `list_store_coupons` to see exactly how discounts affect purchase volume. Because LlamaIndex stores the actual API responses, your analytical queries rely on real transaction histories rather than statistical guesses.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Sellfy. 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.

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Common questions about Sellfy MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Set up a `BasicMCPClient`, wrap it in `McpToolSpec`, and call `await mcp_tool_spec.to_tool_list_async()`. Feed the resulting list into your `FunctionAgent`.

Yes. You configure the agent to call `list_customers` and `get_customer_details`. The framework indexes the buyer profiles so you can run semantic queries against your user base.

It can. While LlamaIndex excels at RAG, its function agents can execute write operations. If you grant access to `create_product`, the agent can generate a title and price, then push the new item live.

You need to refresh your index periodically. The agent calls `list_products` on a schedule to fetch the latest catalog state. This ensures your semantic search results don't point to deleted items.

The MCP Server pulls sensitive transaction records like physical shipping addresses, payment statuses, and buyer emails via `get_order_details`. LlamaIndex processes these strings locally in your environment. If you use a hosted vector database, you are responsible for encrypting that PII at rest.

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