How to Use the Zenedu MCP in Google ADK

Q: How do I manage bot funnels with Zenedu and the Google ADK?

You call listbotfunnels. The resulting structured data is then passed to your LlmAgent, which uses its long-context reasoning (1M+ tokens) to analyze how those funnels interact with BigQuery datasets.

Q: Does Zenedu help me check bot product data using the Google ADK?

Yes. You use listbotproducts. This function provides a clean list of products, which your agent can then join with pricing or inventory data residing in BigQuery.

Q: Is subscriber management supported by Zenedu and the Google ADK?

Definitely. Use listbotsubscribers to pull user records. Your agent processes this list natively within Google's cloud ecosystem, keeping your data centralized.

Act on massive datasets in Google ADK using the Zenedu MCP Server.

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Connect Zenedu MCP to Google ADK

Create your Vinkius account to connect Zenedu 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 Zenedu with Google ADK

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Perplexity

Analyze large user bases with the MCP Server.

The `list_bot_subscribers` tool gives you structured access to all bot users. Because your agent integrates with BigQuery, it can process these records against petabytes of historical data. Your Google ADK agents don't just read a list; they join that list to other datasets in Vertex AI for deep context and analysis.

Map out the entire bot system structure with the MCP Server.

Run `list_bots` followed by `list_bot_funnels`. This allows your agent to map every possible pathway a user can take through your communication systems. This is perfect for Google ADK, where you need to understand the entire state graph of your enterprise application.

Get full transactional history with the MCP Server.

The `list_bot_orders` and `list_bot_products` tools pull specific sales data points. You get records that include product IDs, quantities, and associated order metadata. Your agent can use this structured output to trigger complex financial logic within Google Cloud infrastructure.

Setup guide

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

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Common questions about Zenedu MCP in Google ADK

You call `list_bot_funnels`. The resulting structured data is then passed to your LlmAgent, which uses its long-context reasoning (1M+ tokens) to analyze how those funnels interact with BigQuery datasets.

Yes. You use `list_bot_products`. This function provides a clean list of products, which your agent can then join with pricing or inventory data residing in BigQuery.

Definitely. Use `list_bot_subscribers` to pull user records. Your agent processes this list natively within Google's cloud ecosystem, keeping your data centralized.

The limits depend on your BigQuery setup. However, for simply retrieving bot metadata like `list_bots` or `list_bot_offers`, the MCP Server handles the structured retrieval efficiently.

This server touches subscriber records, order history, and product names. The agent ensures that these list functions only expose the necessary fields for your specific task.

Use it with your favorite AI tools

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