How to Use the Sobot MCP in Google ADK

Build enterprise agents on Google ADK that manage customer support via Sobot's data models.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Sobot MCP to Google ADK

Create your Vinkius account to connect Sobot to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Sobot with Google ADK

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Perplexity Vinkius runs on Gemini

Gemini

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Process Complex Support Requests

The agent handles complex, long-context service requests. It can check ticket details using `get_ticket_details` and then use `list_users` to pull all associated customer records into a single context for analysis. The ability to reason over large data sets means it processes multiple inputs—like combining `list_chat_history` with `get_org_summary`—in one go. It keeps everything in scope.

Integrate Internal Knowledge Bases

Need the agent to answer based on internal documents? Use `list_knowledge` and then pull specific articles with `get_knowledge_detail`. The long-context window means it can reference dozens of these sources at once. This is perfect for enterprise environments. Your agents act directly on your company's massive knowledge graph, not just general internet data.

Audit Support Operations

You can monitor agent readiness and team capacity by calling `get_agent_status` or running a roster check with `list_agents`. This gives you real-time metrics on support staff availability. For historical auditing, the MCP Server supports listing all past tickets using `list_tickets`, allowing your agents to analyze trends across years of data.

Setup guide

Set up Sobot 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 Sobot 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="Sobot_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Sobot tools via MCP.",
    tools=mcp_tools,
)

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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 Sobot. 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.

Why Choose Vinkius

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Common questions about Sobot MCP in Google ADK

Sobot exposes its tools for native integration into the Gemini ecosystem. Your agent acts on the MCP Server endpoints using Google's framework, keeping everything within your Google Cloud environment.

Yes. The long-context reasoning capabilities mean you can pass massive amounts of data—like entire chat histories and organizational summaries—to the agent for deep analysis.

The MCP Server touches user details, specifically customer names and account IDs. You control access to this sensitive information by defining which tools your agent can run via the `list_users` tool.

It’s designed for enterprise scale, meaning it's built to handle petabytes of data and complex workflows. You get reliability backed by the full power of BigQuery integration.

Your agent uses Vertex AI connections, which allows you to centralize all logging and tracking within your existing Google Cloud infrastructure for unified auditing.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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