How to Use the Udesk / 沃丰科技 MCP in Google ADK

Q: Can Udesk / 沃丰科技 list all articles using Google ADK?

Yes, you call listarticles to get a current list of available knowledge base content. This helps your agent find the right internal documentation to answer complex customer questions.

Build enterprise agents for Udesk / 沃丰科技 using Google ADK.

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Connect Udesk / 沃丰科技 MCP to Google ADK

Create your Vinkius account to connect Udesk / 沃丰科技 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 Udesk / 沃丰科技 with Google ADK

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Getting Full Ticket History with Google ADK

You can pull up a ticket's entire lifecycle by calling `get_ticket` and then running `get_ticket_replies`. This provides the full context, allowing your agent to analyze everything said. The ability to ingest large amounts of data means your Gemini model won't lose context when reviewing long conversations.

Querying Multiple Customers with Google ADK

Want a list of everyone who contacts support? Run `list_customers` or use `get_customer` for specifics. Since this agent integrates natively with BigQuery, it can perform complex lookups across your entire enterprise data set—it's way beyond simple database queries.

Managing Support Staff and Groups with Google ADK

If you need to check who handles support, the agent supports listing agents via `list_agents`. You can also get a list of predefined groups using `list_groups`, which helps route tickets correctly. The native integration means your agent can act on this structure while running complex reasoning against BigQuery.

Setup guide

Set up Udesk / 沃丰科技 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 Udesk / 沃丰科技 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="Udesk / 沃丰科技_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Udesk / 沃丰科技 tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Udesk / 沃丰科技 MCP in Google ADK

The MCP Server exposes tools that the LlmAgent uses. Because it's built for enterprise cloud environments, your agent can act on this data while also leveraging BigQuery and Vertex AI models.

It manages support record data. This includes ticket metadata, customer profiles, and agent group details. The long-context reasoning capability means it handles massive amounts of information accurately.

Yes, you call `list_articles` to get a current list of available knowledge base content. This helps your agent find the right internal documentation to answer complex customer questions.

It's built specifically for that scale. The integration with BigQuery and Vertex AI means you are building enterprise-grade agents that can operate on your existing, massive data infrastructure.

The server touches support record data. Since the agent runs within a controlled cloud environment and supports native BigQuery integration, it maintains rigorous control over sensitive customer information.

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