How to Use the Yonyou / 用友 MCP in Google ADK

Q: How do I use Yonyou / 用友 data with Google ADK?

You initialize the agent using McpToolset and pass it to your LlmAgent. The system then exposes all available tools, like listsuppliers, for your long-context models to act upon.

Q: What specific types of records does Yonyou / 用友 use with Google ADK?

The server handles everything from listmaterials (master data) to financial vouchers. You can filter exposed tools if you only need a subset of the functionality.

Q: What is the data privacy concern when running Yonyou / 用友 with Google ADK?

The server touches listvouchers. When building your agent, remember to restrict access to this financial voucher data type to only what's necessary for the specific task.

Build enterprise agents on Google Cloud that act on Yonyou / 用友 ERP data using Google ADK.

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Connect Yonyou / 用友 MCP to Google ADK

Create your Vinkius account to connect Yonyou / 用友 to Google ADK 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 Yonyou / 用友 with Google ADK

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Perplexity

Accessing Core Organizational Data

To understand the structure of your business, you use `get_org_info` to pull organizational metadata. You can also list all available ERP customers using `list_customers`. This lets your agent map out relationships and hierarchies. Because this MCP Server is designed for Google Cloud, your agent integrates seamlessly with BigQuery, giving it a massive data context.

Handling Full Sales and Purchase Cycles

For end-to-end tracking, the `get_sales_order` tool pulls specific details on completed sales. Meanwhile, `list_purchase_orders` provides a comprehensive view of pending buys across Yonyou / 用友 systems. With Gemini models and their 1M+ token context window, your agent can hold all these disparate pieces of data in memory while making complex decisions.

Managing Financial & Inventory Details

When tracking financials, the `list_vouchers` tool lets your agent see recorded transactions. You can also check stock levels using `get_inventory`, or list what materials are available via `list_materials`. This deep integration with Google Cloud means that all these actions—from inventory checks to financial reads—can be logged and analyzed directly in BigQuery.

Setup guide

Set up Yonyou / 用友 MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Yonyou / 用友 tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Yonyou / 用友_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Yonyou / 用友 tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Yonyou / 用友 MCP in Google ADK

You initialize the agent using `McpToolset` and pass it to your LlmAgent. The system then exposes all available tools, like `list_suppliers`, for your long-context models to act upon.

Yes. Since the framework is built on Vertex AI and BigQuery, it's designed for enterprise scale. The 1M+ token context window lets you process massive amounts of related business information.

You can. The agent is built to consume external services like this MCP Server, allowing your Gemini models to read purchase orders and sales details directly from the source.

The server handles everything from `list_materials` (master data) to financial vouchers. You can filter exposed tools if you only need a subset of the functionality.

The server touches `list_vouchers`. When building your agent, remember to restrict access to this financial voucher data type to only what's necessary for the specific task.

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