How to Use the Mokaform MCP in Google ADK

Q: How do I initialize the Mokaform MCP Server in my Google ADK agent?

Use the McpToolset class with StreamableHttpServerParameters pointing to your Vinkius URL. Pass this toolset directly into your LlmAgent tool list to expose all eight form-management capabilities.

Q: Can my agent export workspace structures to Vertex AI?

Yes. Your agent can call listworkspaces and listforms to extract structural configurations, then feed those schemas into Vertex AI to train custom classification models.

Deploy enterprise Gemini agents via Google ADK to analyze and structure complex Mokaform feedback loops.

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Connect Mokaform MCP to Google ADK

Create your Vinkius account to connect Mokaform 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 Mokaform with Google ADK

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Feed Form Structures into Google ADK Agents

Large-scale surveys often have hundreds of conditional branches. With the Google ADK, you can pull entire form architectures using `get_form` and `list_forms` directly into Gemini's massive context window. The model analyzes the entire logic tree at once. This allows your agent to suggest structural improvements or spot dead-end logic paths without running out of memory.

Run BigQuery Analytics on Survey Responses

You can connect your Google ADK agent to both BigQuery and this MCP Server to cross-reference data. The agent pulls survey results with `list_responses` and `get_response`, then matches them against offline customer profiles in your warehouse. This setup lets you run deep sentiment analysis or update customer records automatically. You don't need to write manual ETL pipelines to sync your surveys with your data cloud.

Organize Workspaces via Google Cloud Infrastructure

Manage complex organizational setups using native Google ADK tools. The MCP Server lets the agent use `list_workspaces` to identify target directories, then runs `create_form` to deploy template surveys across different departments. If a department retires a survey, the agent cleans up the workspace by invoking `delete_response` on old test submissions. Everything runs under your existing Google Cloud IAM policies.

Setup guide

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

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Common questions about Mokaform MCP in Google ADK

Use the `McpToolset` class with `StreamableHttpServerParameters` pointing to your Vinkius URL. Pass this toolset directly into your `LlmAgent` tool list to expose all eight form-management capabilities.

Yes, you can use the optional tool names filter in the toolset configuration. This lets you restrict access to read-only tools like `list_responses` while blocking destructive tools like `delete_response`.

Gemini uses its reasoning capabilities to parse the conditional rules returned by `get_form`. It maps out the decision tree and determines exactly which question to present next to a user.

Yes. Your agent can call `list_workspaces` and `list_forms` to extract structural configurations, then feed those schemas into Vertex AI to train custom classification models.

All workspace metadata and form schemas pass through ephemeral, zero-trust V8 sandboxes via the MCP Server. No persistent storage of your proprietary survey structures occurs on the transport layer.

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