How to Use the Zeev MCP in Google ADK

Q: How do I check if a specific process is running using Zeev and Google ADK?

Use getrequest to retrieve the full details of an existing request. This allows your agent to see exactly which state the workflow has reached within Zeev.

Q: Where can I find a list of all available workflows for Zeev and Google ADK?

The listprocesses tool gives you an authoritative catalog. It shows every possible process definition, so your agent knows its operational boundaries.

Q: If a user needs to know their current identity for Zeev and Google ADK?

Your agent calls getme to pull the necessary user information. This is critical data when the system requires knowing who is making or approving changes.

Q: What if I need to track a specific task in Zeev and Google ADK?

You get granular details about any single job using gettask. This confirms which task needs attention, helping the agent maintain visibility over complex approval chains.

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Connect Zeev MCP to Google ADK

Create your Vinkius account to connect Zeev 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 Zeev with Google ADK

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Initiate and monitor workflows.

When you need to start a business process, your agent uses `create_request`. This function kicks off new requests in Zeev, letting it follow the defined BPMN logic. To keep tabs on everything, calling `list_requests` gives an immediate overview of all running instances.

Manage work delegation and completion.

The agent sends tasks to people via `delegate_task`, assigning clear ownership for the next step. Once the job is done, you use `finish_task` to mark it complete in Zeev. You can check who needs to take action using `list_tasks`.

Inspect and modify process definitions.

Before running anything, your agent checks the blueprint by calling `get_process` or listing all available ones with `list_processes`. This ensures it understands the rules of the workflow before attempting to manipulate a state using tools like `cancel_request`.

Setup guide

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

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Common questions about Zeev MCP in Google ADK

Use `get_request` to retrieve the full details of an existing request. This allows your agent to see exactly which state the workflow has reached within Zeev.

The `list_processes` tool gives you an authoritative catalog. It shows every possible process definition, so your agent knows its operational boundaries.

Your agent calls `get_me` to pull the necessary user information. This is critical data when the system requires knowing who is making or approving changes.

You get granular details about any single job using `get_task`. This confirms which task needs attention, helping the agent maintain visibility over complex approval chains.

The server handles process definitions, request instances, and task records. These are all types of structured workflow data that your agent interacts with via the API.

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