How to Use the Zenkit MCP in Google ADK

Q: Is Zenkit good for complex enterprise data tasks with Google ADK?

Yes. It provides the necessary CRUD operations (createentry, updateentry) to let your agent write validated, structured results back into your operational systems.

Q: How can I check the structure of a Zenkit list using Google ADK?

You use listelements. This tool shows all defined fields and their expected data types, giving your agent the schema it needs before attempting to write an entry.

Act on structured enterprise data with Google ADK and Zenkit MCP Server.

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Connect Zenkit MCP to Google ADK

Create your Vinkius account to connect Zenkit 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 Zenkit with Google ADK

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Manage core data records for Google ADK

Your agent needs to make changes, right? It can call `create_entry` to add a new record or `update_entry` to modify an existing one. These tools give your Gemini-powered workflows the ability to write back structured results. You'll also use `delete_entry` when data is obsolete. This gives the agent full control over maintaining accurate, up-to-date records within any Zenkit list.

Get a map of available lists for Google ADK

Before acting on data, you need to know where it lives. Use `list_workspaces` and then follow up with `get_list_details`. This gives your agent the full directory of all structured repositories. Need to know what fields are available within a specific list? The `list_elements` tool shows the schema, so the agent knows exactly which parameters it needs for any given task.

Retrieve and validate data context using MCP Server

The agent can pull full details on its operational environment using `get_workspace_details`. This ensures that the large-context reasoning happening in Gemini models has the right scope. It also lets you check existing items with `list_entries` or get deep context about a single list with `get_list_details`. It’s how your agent grounds its massive knowledge base in real, current data.

Setup guide

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

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Common questions about Zenkit MCP in Google ADK

The MCP Server exposes Zenkit's API calls as tools. Your LlmAgent uses these tools to interact with structured data, allowing Gemini models to perform actions beyond just generating text.

It handles core list components: workspaces, lists, and individual entries. The agent operates on structured JSON payloads that define field values for new or updated records.

Yes. It provides the necessary CRUD operations (`create_entry`, `update_entry`) to let your agent write validated, structured results back into your operational systems.

The server specifically touches 'list entries' data. When developing agents that handle sensitive information, always restrict access to specific fields rather than granting full list write permissions.

You use `list_elements`. This tool shows all defined fields and their expected data types, giving your agent the schema it needs before attempting to write an entry.

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