How to Use the YoneTeam MCP in LlamaIndex

Q: Does YoneTeam provide data on deliveries for LlamaIndex?

Yes. You can run listdeliveries, and the resulting schedule data gets indexed. This lets you search through past delivery records semantically.

Q: What kind of data does YoneTeam expose regarding demands for LlamaIndex?

The server exposes purchasing demand data types via listdemands. Indexing this allows users to query historical trends in company purchasing needs.

Ground your decisions in facts: LlamaIndex indexes YoneTeam's operational data for robust knowledge search.

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Connect YoneTeam MCP to LlamaIndex

Create your Vinkius account to connect YoneTeam 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 YoneTeam with LlamaIndex

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Search Purchase Histories

Don't just call tools; index the results. You can run `list_purchase_orders` and feed that output into your vector store. Later, you query it to get answers grounded in actual past API data.

Catalog Querying

The tool output from `list_materials_catalog` becomes part of a searchable knowledge base. Need to know if a specific material was ordered before? Index the catalog results, and your agent can answer that query.

Supplier Comparison via Indexing

You can run `list_suppliers` and index the list of approved vendors. This lets you ask complex questions like, 'Which suppliers provided materials in Q3?' The resulting knowledge base grounds your answer.

Setup guide

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

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Common questions about YoneTeam MCP in LlamaIndex

LlamaIndex indexes the results from MCP tools into a vector store. This means you can query past API calls or configurations and get answers based on that live, actual data.

It stops hallucinations. Instead of just calling a tool, LlamaIndex combines the output from tools like `get_order_details` and documents into one unified, queryable index.

Yes. You can run `list_deliveries`, and the resulting schedule data gets indexed. This lets you search through past delivery records semantically.

The server exposes purchasing demand data types via `list_demands`. Indexing this allows users to query historical trends in company purchasing needs.

The server handles operational data types, including purchase order details and supplier offers. This information can be indexed into your knowledge base.

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